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+ "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Undergraduate Medical Education\n - Health Literacy\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n\n## Abstract\n\n\n**Background / Hintergrund**: {{< lipsum 1 >}}\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background\n\n\n### Broad problem\n\nHealth literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n\n### Theoretical and/or empirical focus of the problem\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement\n\nEspecially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n\nWhen providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n\n### Statement of study intent\n\nWe performed a study of medical students to investigate the following questions:\n\n1. What is ...\n2. Why are ...\n\n\n\n## Methods\n\n\n### Setting and subjects\n\nOur study was conducted at Medical Faculty of Münster, Germany. It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year).\n\n### Study design\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\n\n{{< lipsum 1 >}}\n\n\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n)\n\n# Read the data\ndf_penguins = read.csv(\"penguins.csv\")\n```\n\n### Outcome Measures\n\n...\n\n### Statistical methods\n\n...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # Download the dataset\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # Read the data\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # Build a scatterplot\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species),\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n```\n\n\n\n\n## Results\n\n### Recruitment Process and Demographic Characteristics\n\nThe recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n### Primary and secondary Outcomes\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n\n\n## Discussion\n\n### Summary\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n\n{{< lipsum 1 >}}\n\n\n\n### Limitation: study design\n\n...\n\n### Integration with prior work\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n\n{{< lipsum 1 >}}\n\n\n\n### Implications for research\n\n...\n\n### Conclusions\n\n...\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n### Ethics approval and consent to participate\n\n\n{{< lipsum 1 >}}\n\n\n\n### Consent for publication\n\nNot applicable\n\n### Availability of data and materials\n\nThe original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n\n### Competing interests\n\nThe authors declare that they have no competing interests.\n\n### Funding\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions\n\n\n{{< lipsum 1 >}}\n\n\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \n**Hendrik Friederichs:** Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments\n\nThe authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n\n",
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- "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Undergraduate Medical Education\n - Health Literacy\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n\n**Background / Hintergrund**: {{< lipsum 1 >}}\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background\n\n\n### Broad problem\n\nHealth literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n\n### Theoretical and/or empirical focus of the problem\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement\n\nEspecially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n\nWhen providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n\n### Statement of study intent\n\nWe performed a study of medical students to investigate the following questions:\n\n1. What is ...\n2. Why are ...\n\n\n\n## Methods\n\n\n### Setting and subjects\n\nOur study was conducted at Medical Faculty of Münster, Germany. It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year).\n\n### Study design\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\n{{< lipsum 1 >}}\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n)\n\n# Read the data\ndf_penguins = read.csv(\"penguins.csv\")\n```\n\n### Outcome Measures\n\n...\n\n### Statistical methods\n\n...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # Download the dataset\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # Read the data\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # Build a scatterplot\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species),\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n```\n1. \n2. \n3. \n\n\n\n## Results\n\n### Recruitment Process and Demographic Characteristics\n\nThe recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n### Primary and secondary Outcomes\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n\n\n## Discussion\n\n### Summary\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n{{< lipsum 1 >}}\n\n### Limitation: study design\n\n...\n\n### Integration with prior work\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n{{< lipsum 1 >}}\n\n### Implications for research\n\n...\n\n### Conclusions\n\n...\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n### Ethics approval and consent to participate\n\n{{< lipsum 1 >}}\n\n### Consent for publication\n\nNot applicable\n\n### Availability of data and materials\n\nThe original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n\n### Competing interests\n\nThe authors declare that they have no competing interests.\n\n### Funding\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions\n\n{{< lipsum 1 >}}\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \n**Hendrik Friederichs:** Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments\n\nThe authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n\n",
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+ "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Undergraduate Medical Education\n - Health Literacy\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n\n**Background / Hintergrund**: {{< lipsum 1 >}}\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background\n\n\n### Broad problem\n\nHealth literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n\n### Theoretical and/or empirical focus of the problem\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement\n\nEspecially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n\nWhen providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n\n### Statement of study intent\n\nWe performed a study of medical students to investigate the following questions:\n\n1. What is ...\n2. Why are ...\n\n\n\n## Methods\n\n\n### Setting and subjects\n\nOur study was conducted at Medical Faculty of Münster, Germany. It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year).\n\n### Study design\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\n{{< lipsum 1 >}}\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n)\n\n# Read the data\ndf_penguins = read.csv(\"penguins.csv\")\n```\n\n### Outcome Measures\n\n...\n\n### Statistical methods\n\n...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # Download the dataset\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # Read the data\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # Build a scatterplot\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species),\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n```\n\n\n\n\n## Results\n\n### Recruitment Process and Demographic Characteristics\n\nThe recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n### Primary and secondary Outcomes\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n\n\n## Discussion\n\n### Summary\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n{{< lipsum 1 >}}\n\n### Limitation: study design\n\n...\n\n### Integration with prior work\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n{{< lipsum 1 >}}\n\n### Implications for research\n\n...\n\n### Conclusions\n\n...\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n### Ethics approval and consent to participate\n\n{{< lipsum 1 >}}\n\n### Consent for publication\n\nNot applicable\n\n### Availability of data and materials\n\nThe original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n\n### Competing interests\n\nThe authors declare that they have no competing interests.\n\n### Funding\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions\n\n{{< lipsum 1 >}}\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \n**Hendrik Friederichs:** Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments\n\nThe authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n\n",
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+ "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Undergraduate Medical Education\n - Health Literacy\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n\n**Background / Hintergrund**: {{< lipsum 1 >}}\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background\n\n\n### Broad problem\n\nHealth literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n\n### Theoretical and/or empirical focus of the problem\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement\n\nEspecially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n\nWhen providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n\n### Statement of study intent\n\nWe performed a study of medical students to investigate the following questions:\n\n1. What is ...\n2. Why are ...\n\n\n\n## Methods\n\n\n### Setting and subjects\n\nOur study was conducted at Medical Faculty of Münster, Germany. It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year).\n\n### Study design\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\n{{< lipsum 1 >}}\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n)\n\n# Read the data\ndf_penguins = read.csv(\"penguins.csv\")\n```\n\n### Outcome Measures\n\n...\n\n### Statistical methods\n\n...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # Download the dataset\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # Read the data\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # Build a scatterplot\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species),\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n```\n\n\n\n\n## Results\n\n### Recruitment Process and Demographic Characteristics\n\nThe recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n### Primary and secondary Outcomes\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n\n\n## Discussion\n\n### Summary\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n{{< lipsum 1 >}}\n\n### Limitation: study design\n\n...\n\n### Integration with prior work\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n{{< lipsum 1 >}}\n\n### Implications for research\n\n...\n\n### Conclusions\n\n...\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n### Ethics approval and consent to participate\n\n{{< lipsum 1 >}}\n\n### Consent for publication\n\nNot applicable\n\n### Availability of data and materials\n\nThe original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n\n### Competing interests\n\nThe authors declare that they have no competing interests.\n\n### Funding\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions\n\n{{< lipsum 1 >}}\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \n**Hendrik Friederichs:** Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments\n\nThe authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n\n",
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@@ -0,0 +1,15 @@
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+ "result": {
+ "engine": "knitr",
+ "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Undergraduate Medical Education\n - Health Literacy\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n\n**Background / Hintergrund**: {{< lipsum 1 >}}\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background\n\n\n### Broad problem\n\nHealth literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n\n### Theoretical and/or empirical focus of the problem\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement\n\nEspecially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n\nWhen providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n\n### Statement of study intent\n\nWe performed a study of medical students to investigate the following questions:\n\n1. What is ...\n2. Why are ...\n\n\n\n## Methods\n\n\n### Setting and subjects\n\nOur study was conducted at Medical Faculty of Münster, Germany. It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year).\n\n### Study design\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\n{{< lipsum 1 >}}\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n)\n\n# Read the data\ndf_penguins = read.csv(\"penguins.csv\")\n```\n\n### Outcome Measures\n\n...\n\n### Statistical methods\n\n...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # Download the dataset\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # Read the data\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # Build a scatterplot\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species),\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n```\n\n\n\n\n## Results\n\n### Recruitment Process and Demographic Characteristics\n\nThe recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n### Primary and secondary Outcomes\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n\n\n## Discussion\n\n### Summary\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n{{< lipsum 1 >}}\n\n### Limitation: study design\n\n...\n\n### Integration with prior work\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n{{< lipsum 1 >}}\n\n### Implications for research\n\n...\n\n### Conclusions\n\n...\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n### Ethics approval and consent to participate\n\n{{< lipsum 1 >}}\n\n### Consent for publication\n\nNot applicable\n\n### Availability of data and materials\n\nThe original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n\n### Competing interests\n\nThe authors declare that they have no competing interests.\n\n### Funding\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions\n\n{{< lipsum 1 >}}\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \n**Hendrik Friederichs:** Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments\n\nThe authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n\n",
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diff --git a/_quarto.yml b/_quarto.yml
index 85517f4ad6ef154f1e905f0191825d6b5c84f508..df2c6d60ae96a74c9842f9cbdb32bc103a3760eb 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -18,25 +18,25 @@ format:
comments:
hypothesis: true
docx:
- output-file: Friederichs_et_al.docx
+ output-file: Soler_Wenglein_et_al.docx
typst:
keep-typ: true
- output-file: Typst_Friederichs_et_al.pdf
- pdf:
- output-file: Friederichs_et_al.pdf
+ output-file: Typst_Soler_Wenglein_et_al.pdf
+# pdf:
+# output-file: Friederichs_et_al.pdf
AG_7-pdf:
- output-file: Friederichs_et_al_AG_7.pdf
+ output-file: Soler_Wenglein_et_al_AG_7.pdf
keep-tex: true
arXiv-pdf:
- output-file: Friederichs_et_al_arXiv.pdf
+ output-file: Soler_Wenglein_et_al_arXiv.pdf
keep-tex: true
linenumbers: true # Add (continuous) line numbers?
doublespacing: true # Double space the PDF output?
runninghead: "A Preprint" # The text on the top of each page of the output
- apaquarto-docx:
- output-file: APA_Friederichs_et_al.docx
+# apaquarto-docx:
+# output-file: APA_Friederichs_et_al.docx
apaquarto-pdf:
- output-file: APA_Friederichs_et_al.pdf
+ output-file: APA_Soler_Wenglein_et_al.pdf
# can be jou (journal), man (manuscript), stu (student), or doc (document)
# for now, tables and figures do not render properly in jou mode.
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old mode 100755
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+ %&-line parsing enabled.
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+File: size12.clo 2023/05/17 v1.4n Standard LaTeX file (size option)
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+
+\title{Assessment of graph literacy among German medical students -- a
+cross-sectional study to assess graph interpretation skills}
+\subtitle{Draft of the manuscript}
+
+
+
+
+\authorsnames[{1},{1}]{
+Janina Soler Wenglein,Hendrik Friederichs
+}
+
+\authorsaffiliations{
+{AG Medical Education, Universität Bielefeld, Medizinische Fakultät
+OWL}}
+
+\date{2024-01-23}
+\abstract{\textbf{Background / Hintergrund}: Etiam quis tortor luctus,
+pellentesque ante a, finibus dolor. Phasellus in nibh et magna pulvinar
+malesuada. Ut nisl ex, sagittis at sollicitudin et, sollicitudin id
+nunc. In id porta urna. Proin porta dolor dolor, vel dapibus nisi
+lacinia in. Pellentesque ante mauris, ornare non euismod a, fermentum ut
+sapien. Proin sed vehicula enim. Aliquam tortor odio, vestibulum vitae
+odio in, tempor molestie justo. Praesent maximus lacus nec leo maximus
+blandit.
+
+\textbf{Methods / Methoden}: \ldots{}
+
+\textbf{Results / Ergebnisse}: \ldots{}
+
+\textbf{Conclusio / Schlussfolgerungen}: \ldots{}}
+% % \addbibresource{references.bib}
+%
+\keywords{Undergraduate Medical Education, Health Literacy}
+
+\authornote{\par{\addORCIDlink{Janina Soler
+Wenglein}{0000-2222-1111-3333}}\par{\addORCIDlink{Hendrik
+Friederichs}{0000-0001-9671-5235}}
+\par{ }
+\par{ }
+\par{Correspondence concerning this article should be addressed to Hendrik
+Friederichs, AG Medical Education, Universität Bielefeld, Medizinische
+Fakultät OWL, Universitätsstr.
+25, Bielefeld, NRW 33615, Deutschland, Email: hendrik.friederichs@uni-bielefeld.de}
+}
+
+\begin{document}
+\maketitle
+\textsuperscript{1} Universität Bielefeld, Medizinische Fakultät OWL
+
+\textsuperscript{*} Correspondence:
+\href{mailto:hendrik.friederichs@uni-bielefeld.de}{Hendrik Friederichs
+\textless{}hendrik.friederichs@uni-bielefeld.de\textgreater{}}
+
+\begin{tcolorbox}[enhanced jigsaw, opacitybacktitle=0.6, breakable, coltitle=black, opacityback=0, colbacktitle=quarto-callout-caution-color!10!white, title=\textcolor{quarto-callout-caution-color}{\faFire}\hspace{0.5em}{IN PROGRESS \ldots{}}, colframe=quarto-callout-caution-color-frame, left=2mm, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, colback=white, bottomtitle=1mm, toprule=.15mm, toptitle=1mm, arc=.35mm, titlerule=0mm]
+
+This manuscript is a work in progress. However, thank you for your
+interest. Please feel free to visit this web site again at a later date.
+
+\textcolor{grey}{\emph{Dieses Manuskript ist noch in Arbeit. Wir danken
+Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu
+einem späteren Zeitpunkt noch einmal \ldots{}}}
+
+\end{tcolorbox}
+
+\begin{tcolorbox}[enhanced jigsaw, opacitybacktitle=0.6, breakable, coltitle=black, opacityback=0, colbacktitle=quarto-callout-tip-color!10!white, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{STRUKTUR DES MANUSKRIPTS}, colframe=quarto-callout-tip-color-frame, left=2mm, rightrule=.15mm, leftrule=.75mm, bottomrule=.15mm, colback=white, bottomtitle=1mm, toprule=.15mm, toptitle=1mm, arc=.35mm, titlerule=0mm]
+
+\textcolor{grey}{\textbf{Relevantes Problem:} Gesundheitskompetenz
+beruht auf verschiedenen Fähigkeiten zur Informationsverarbeitung. Sie
+ist für Ärzte essentiell, um medizinische Berichte, Behandlungen und
+Studienergebnisse zu verstehen, Patienten aufzuklären, klinische
+Entscheidungen zu treffen und effektiv im medizinischen Team zu
+kommunizieren.\\
+\textbf{Fokussiertes Problem:} Graph Literacy, als Teil der
+Gesundheitskompetenz, ist wichtig für das Lesen und Verstehen von
+Grafiken. Es umfasst das Entziffern von Zeichen und Symbolen und ist eng
+mit anderen Formen der Literacy verbunden. Dies ist besonders relevant
+in Bereichen wie der medizinischen Forschung, wo Grafiken zur
+Vermittlung komplexer Informationen genutzt werden. Fehldarstellungen
+können jedoch die Informationsaufnahme beeinflussen.\\
+\textbf{Gap des Problems:} Graph Literacy beeinflusst das
+Risikoverständnis und Entscheidungsverhalten, wobei Studien
+hauptsächlich Patienten und Ärzte im Umgang mit grafischen Darstellungen
+betrachten.\\
+\textbf{Lösung?:} Für Ärzte, die Menschen mit geringerer
+Gesundheitskompetenz betreuen, ist es wichtig, selbst eine hohe
+Kompetenz im Verständnis visueller Darstellungen zu haben. Dies
+beeinflusst die Entscheidungsfindung der Patienten und Ärzte.\\
+\textbf{Forschungsfragen:} Daher wurde eine Studie mit
+Medizinstudierenden durchgeführt, um ihre Fähigkeit zur Interpretation
+visuell dargestellter medizinischer Informationen zu untersuchen.\\
+\textbf{Studienpopulation:} Medizinstudierende\\
+\textbf{Studiendesign:} Beobachtungsstudie\\
+\textbf{Datenerhebung:} Graph Literacy Scale\\
+\textbf{Ergebnisparameter:} Anzahl der richtigen Antworten insgesamt.\\
+\textbf{Statistik:} Bestimmung der Prozentwerte für die absolute und
+z-Scores und Percentilen für die relative Bewertung der Leistungen im
+Studienverlauf.}
+
+\end{tcolorbox}
+
+\subsection{Background}\label{background}
+
+\subsubsection{Broad problem}\label{broad-problem}
+
+Health literacy depends on diverse aspects of skills in processing
+information. To adequately understand medical reports, treatments and
+study results a set of abilities is needed. Thinking of future
+physicians one can imagine a multitude of situations where a high health
+literacy is required: whenever talking with patients about medical data,
+consenting in treatments and educating patients about diseases, making
+clinical decisions depending on laboratory results, imaging and study
+results, understanding evidence, interpretation of epidemiological data
+and communication in medical teams.
+
+\subsubsection{Theoretical and/or empirical focus of the
+problem}\label{theoretical-andor-empirical-focus-of-the-problem}
+
+One important aspect of health literacy is graph literacy, meaning the
+reading and understanding of graphs. This process is depending on
+decoding and interpreting signs and symbols and known as semiotic
+activity. Thus, the ability to understand graphs should not be
+considered isolatioted from other forms of literacy. It is an integral
+part of the ability to process and communicate information effectively
+in a world that is increasingly dependent on data and its visual
+representation.
+
+Processing those visual representations is essential for understanding
+scientific and statistical data
+{[}1{]}\marginpar{\begin{footnotesize}
+\begin{CSLReferences}{2}{0}
+\bibitem[\citeproctext]{ref-friel2001making}
+1. Friel SN, Curcio FR, Bright GW. Making sense of graphs: Critical
+factors influencing comprehension and instructional implications.
+Journal for Research in mathematics Education. 2001;32:124--58.
+\end{CSLReferences}
+\vspace{2mm}\par\end{footnotesize}}
+and particularly relevant in areas such as medical research where graphs
+and data visualizations are frequently used to convey complex
+information. A personal understanding of the representations is
+essential when preparing data for communication in order to ensure
+adequate knowledge transfer to others (Cooper et al, 2002). But
+misleading representations (either through deliberate manipulation or
+unintentionally through errors or incompleteness) can also have a
+significant influence on the reception of information by the recipient
+(Melnik-Leroy, 2023).
+
+In summary it ca be said that graph literacy, as a form of semiotic
+activity, is a crucial component of overall literacy (Roth, 2002). It
+can have an impact on risk comprehension (Okan, 2013), suggesting that a
+higher graph literacy may be associated with a better decision-making
+performance. However, studies of graph literacy mainly refer to patients
+(Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to
+interpret graphical representations.
+
+\subsubsection{Focused problem
+statement}\label{focused-problem-statement}
+
+Especially for those advising and informing people with less health and
+graph literacy, it is important to achieve high competence in graph
+literacy themselves. But lack of understanding of visual representations
+can significantly impact decision making for patients and for (future)
+medical doctors.
+
+When providing information to patients, medical doctors must be aware
+about patient health literacy and about their own. Therefore, we
+conducted a cohort study with medical students for understanding their
+ability to interpret medical information provided visually.
+
+\subsubsection{Statement of study
+intent}\label{statement-of-study-intent}
+
+We performed a study of medical students to investigate the following
+questions:
+
+\begin{enumerate}
+\def\labelenumi{\arabic{enumi}.}
+\tightlist
+\item
+ What is \ldots{}
+\item
+ Why are \ldots{}
+\end{enumerate}
+
+\subsection{Methods}\label{methods}
+
+\subsubsection{Setting and subjects}\label{setting-and-subjects}
+
+Our study was conducted at Medical Faculty of Münster, Germany. It takes
+six years to complete a course in medical school in Germany, with
+students enrolled directly from secondary schools. The course of study
+is divided into a pre-clinical section (the first two years) and a
+clinical section (the last four years). To improve students' clinical
+experience, they are rotated in various hospital departments during
+their final year (``clinical/practical'' year).
+
+\subsubsection{Study design}\label{study-design}
+
+The participants were asked to complete the graph literacy scale
+voluntarily and anonymously.
+
+\subsubsection{Ethical approval}\label{ethical-approval}
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
+posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
+rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
+in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
+amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
+non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
+volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
+amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
+at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
+parturient montes, nascetur ridiculus mus.
+
+\subsubsection{Data collection}\label{data-collection}
+
+Data collection for this study was determined à priori as follows:
+
+\ldots{}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{\#| context: setup}
+
+\NormalTok{\# Download a dataset}
+\NormalTok{download.file(}
+\NormalTok{ \textquotesingle{}https://raw.githubusercontent.com/coatless/raw{-}data/main/penguins.csv\textquotesingle{},}
+\NormalTok{ \textquotesingle{}penguins.csv\textquotesingle{}}
+\NormalTok{)}
+
+\NormalTok{\# Read the data}
+\NormalTok{df\_penguins = read.csv("penguins.csv")}
+\end{Highlighting}
+\end{Shaded}
+
+\subsubsection{Outcome Measures}\label{outcome-measures}
+
+\ldots{}
+
+\subsubsection{Statistical methods}\label{statistical-methods}
+
+\ldots{}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{\#| context: interactive}
+
+\NormalTok{\# Download a dataset}
+\NormalTok{download.file(}
+\NormalTok{ \textquotesingle{}https://raw.githubusercontent.com/coatless/raw{-}data/main/penguins.csv\textquotesingle{},}
+\NormalTok{ \textquotesingle{}penguins.csv\textquotesingle{}}
+\NormalTok{) \# Download the dataset}
+
+\NormalTok{\# Read the data}
+\NormalTok{penguins = read.csv("penguins.csv") \# Read the data}
+
+\NormalTok{\# Scatterplot example: penguin bill length versus bill depth}
+\NormalTok{ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill\_length\_mm, y = bill\_depth\_mm)) + \# Build a scatterplot}
+\NormalTok{ ggplot2::geom\_point(ggplot2::aes(color = species, }
+\NormalTok{ shape = species),}
+\NormalTok{ size = 2) +}
+\NormalTok{ ggplot2::scale\_color\_manual(values = c("darkorange","darkorchid","cyan4"))}
+\end{Highlighting}
+\end{Shaded}
+
+\subsection{Results}\label{results}
+
+\subsubsection{Recruitment Process and Demographic
+Characteristics}\label{recruitment-process-and-demographic-characteristics}
+
+The recruitment process is shown in Figure 1. We obtained XX complete
+data sets (return rate YY.Z\%) after contacting \ldots{}
+
+\subsubsection{Primary and secondary
+Outcomes}\label{primary-and-secondary-outcomes}
+
+\begin{figure}[H]
+
+\caption{Beispielgrafik: ein Bild sagt mehr als tausend Worte \ldots{}}
+
+{\centering \includegraphics{Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg}
+
+}
+
+\end{figure}%
+
+\subsection{Discussion}\label{discussion}
+
+\subsubsection{Summary}\label{summary}
+
+After the evaluation of all datasets, the following findings emerged.
+The first is that \ldots{}
+
+\subsubsection{Limitation: study
+population}\label{limitation-study-population}
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis
+posuere ligula sit amet lacinia. Duis dignissim pellentesque magna,
+rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum
+in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit
+amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit,
+non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante
+volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit
+amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim,
+at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis
+parturient montes, nascetur ridiculus mus.
+
+\subsubsection{Limitation: study design}\label{limitation-study-design}
+
+\ldots{}
+
+\subsubsection{Integration with prior
+work}\label{integration-with-prior-work}
+
+Only a few studies provide insights into the graphical and numerical
+skills among medical students.
+
+In a cross-sectional, descriptive study, the researchers applied the
+Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to
+medical students in their final two years of medical school and to
+medical residents. The study included 169 participants, comprising 70\%
+sixth-year seventh-year students, and 30\% residents. The findings
+showed that the mean graph literacy was 10.35. A multiple linear
+regression analysis revealed that higher scores in the Graph Literacy
+Scale were associated with the male gender and younger age. The study
+concluded that numeracy and graph literacy scales' mean scores were high
+among the medical students in this sample
+{[}2{]}\marginpar{\begin{footnotesize}
+\begin{CSLReferences}{2}{0}
+\bibitem[\citeproctext]{ref-mas2018graphical}
+2. Mas G, Tello T, Ortiz P, Petrova D, GarcıÌa-Retamero R. Graphical and
+numerical skills in pre-and postgraduate medical students from a private
+university. Gac Med Mex. 2018;154:163--9.
+\end{CSLReferences}
+\vspace{2mm}\par\end{footnotesize}}.
+
+\subsubsection{Implications for
+practice}\label{implications-for-practice}
+
+Duis ornare ex ac iaculis pretium. Maecenas sagittis odio id erat
+pharetra, sit amet consectetur quam sollicitudin. Vivamus pharetra quam
+purus, nec sagittis risus pretium at. Nullam feugiat, turpis ac accumsan
+interdum, sem tellus blandit neque, id vulputate diam quam semper nisl.
+Donec sit amet enim at neque porttitor aliquet. Phasellus facilisis
+nulla eget placerat eleifend. Vestibulum non egestas eros, eget lobortis
+ipsum. Nulla rutrum massa eget enim aliquam, id porttitor erat luctus.
+Nunc sagittis quis eros eu sagittis. Pellentesque dictum, erat at
+pellentesque sollicitudin, justo augue pulvinar metus, quis rutrum est
+mi nec felis. Vestibulum efficitur mi lorem, at elementum purus
+tincidunt a. Aliquam finibus enim magna, vitae pellentesque erat
+faucibus at. Nulla mauris tellus, imperdiet id lobortis et, dignissim
+condimentum ipsum. Morbi nulla orci, varius at aliquet sed, facilisis id
+tortor. Donec ut urna nisi.
+
+\subsubsection{Implications for
+research}\label{implications-for-research}
+
+\ldots{}
+
+\subsubsection{Conclusions}\label{conclusions}
+
+\ldots{}
+
+\subsection*{References}\label{references}
+\addcontentsline{toc}{subsection}{References}
+
+
+\subsection{Declarations}\label{declarations}
+
+\subsubsection{Ethics approval and consent to
+participate}\label{ethics-approval-and-consent-to-participate}
+
+Etiam quis tortor luctus, pellentesque ante a, finibus dolor. Phasellus
+in nibh et magna pulvinar malesuada. Ut nisl ex, sagittis at
+sollicitudin et, sollicitudin id nunc. In id porta urna. Proin porta
+dolor dolor, vel dapibus nisi lacinia in. Pellentesque ante mauris,
+ornare non euismod a, fermentum ut sapien. Proin sed vehicula enim.
+Aliquam tortor odio, vestibulum vitae odio in, tempor molestie justo.
+Praesent maximus lacus nec leo maximus blandit.
+
+\subsubsection{Consent for publication}\label{consent-for-publication}
+
+Not applicable
+
+\subsubsection{Availability of data and
+materials}\label{availability-of-data-and-materials}
+
+The original data that support the findings of this study are available
+from Open Science Framework (osf.io, see manuscript-URL).
+
+\subsubsection{Competing interests}\label{competing-interests}
+
+The authors declare that they have no competing interests.
+
+\subsubsection{Funding}\label{funding}
+
+The author(s) received no specific funding for this work.
+
+\subsubsection{Authors' contributions}\label{authors-contributions}
+
+Etiam congue quam eget velit convallis, eu sagittis orci vestibulum.
+Vestibulum at massa turpis. Curabitur ornare ex sed purus vulputate,
+vitae porta augue rhoncus. Phasellus auctor suscipit purus, vel
+ultricies nunc. Nunc eleifend nulla ac purus volutpat, id fringilla
+felis aliquet. Duis vitae porttitor nibh, in rhoncus risus. Vestibulum a
+est vitae est tristique vehicula. Proin mollis justo id est tempus
+hendrerit. Praesent suscipit placerat congue. Aliquam eu elit gravida,
+consequat augue non, ultricies sapien. Nunc ultricies viverra ante, sit
+amet vehicula ante volutpat id. Etiam tempus purus vitae tellus mollis
+viverra. Donec at ornare mauris. Aliquam sodales hendrerit ornare.
+Suspendisse accumsan lacinia sapien, sit amet imperdiet dui molestie ut.
+
+\subsubsection{CRediT authorship contribution
+statement}\label{credit-authorship-contribution-statement}
+
+\textbf{Janina Soler Wenglein:} Data curation, Formal analysis,
+Investigation, Methodology, Visualization, Writing - original draft.\\
+\textbf{Hendrik Friederichs:} Conceptualization, Formal analysis,
+Investigation, Methodology, Supervision, Writing - review \& editing,
+Writing - original draft.
+
+\subsubsection{Acknowledgments}\label{acknowledgments}
+
+The authors are grateful for the insightful comments offered by the
+anonymous peer reviewers at Medical Education Online. The generosity and
+expertise of one and all have improved this study in innumerable ways
+and saved us from many errors; those that inevitably remain are entirely
+our own responsibility.
+
+
+\end{document}
diff --git a/index.typ b/index.typ
new file mode 100644
index 0000000000000000000000000000000000000000..5a70e79d985246c6946893f39f77e0277676955a
--- /dev/null
+++ b/index.typ
@@ -0,0 +1,525 @@
+// Some definitions presupposed by pandoc's typst output.
+#let blockquote(body) = [
+ #set text( size: 0.92em )
+ #block(inset: (left: 1.5em, top: 0.2em, bottom: 0.2em))[#body]
+]
+
+#let horizontalrule = [
+ #line(start: (25%,0%), end: (75%,0%))
+]
+
+#let endnote(num, contents) = [
+ #stack(dir: ltr, spacing: 3pt, super[#num], contents)
+]
+
+#show terms: it => {
+ it.children
+ .map(child => [
+ #strong[#child.term]
+ #block(inset: (left: 1.5em, top: -0.4em))[#child.description]
+ ])
+ .join()
+}
+
+// Some quarto-specific definitions.
+
+#show raw.where(block: true): block.with(
+ fill: luma(230),
+ width: 100%,
+ inset: 8pt,
+ radius: 2pt
+ )
+
+#let block_with_new_content(old_block, new_content) = {
+ let d = (:)
+ let fields = old_block.fields()
+ fields.remove("body")
+ if fields.at("below", default: none) != none {
+ // TODO: this is a hack because below is a "synthesized element"
+ // according to the experts in the typst discord...
+ fields.below = fields.below.amount
+ }
+ return block.with(..fields)(new_content)
+}
+
+#let empty(v) = {
+ if type(v) == "string" {
+ // two dollar signs here because we're technically inside
+ // a Pandoc template :grimace:
+ v.matches(regex("^\\s*$")).at(0, default: none) != none
+ } else if type(v) == "content" {
+ if v.at("text", default: none) != none {
+ return empty(v.text)
+ }
+ for child in v.at("children", default: ()) {
+ if not empty(child) {
+ return false
+ }
+ }
+ return true
+ }
+
+}
+
+#show figure: it => {
+ if type(it.kind) != "string" {
+ return it
+ }
+ let kind_match = it.kind.matches(regex("^quarto-callout-(.*)")).at(0, default: none)
+ if kind_match == none {
+ return it
+ }
+ let kind = kind_match.captures.at(0, default: "other")
+ kind = upper(kind.first()) + kind.slice(1)
+ // now we pull apart the callout and reassemble it with the crossref name and counter
+
+ // when we cleanup pandoc's emitted code to avoid spaces this will have to change
+ let old_callout = it.body.children.at(1).body.children.at(1)
+ let old_title_block = old_callout.body.children.at(0)
+ let old_title = old_title_block.body.body.children.at(2)
+
+ // TODO use custom separator if available
+ let new_title = if empty(old_title) {
+ [#kind #it.counter.display()]
+ } else {
+ [#kind #it.counter.display(): #old_title]
+ }
+
+ let new_title_block = block_with_new_content(
+ old_title_block,
+ block_with_new_content(
+ old_title_block.body,
+ old_title_block.body.body.children.at(0) +
+ old_title_block.body.body.children.at(1) +
+ new_title))
+
+ block_with_new_content(old_callout,
+ new_title_block +
+ old_callout.body.children.at(1))
+}
+
+#show ref: it => locate(loc => {
+ let target = query(it.target, loc).first()
+ if it.at("supplement", default: none) == none {
+ it
+ return
+ }
+
+ let sup = it.supplement.text.matches(regex("^45127368-afa1-446a-820f-fc64c546b2c5%(.*)")).at(0, default: none)
+ if sup != none {
+ let parent_id = sup.captures.first()
+ let parent_figure = query(label(parent_id), loc).first()
+ let parent_location = parent_figure.location()
+
+ let counters = numbering(
+ parent_figure.at("numbering"),
+ ..parent_figure.at("counter").at(parent_location))
+
+ let subcounter = numbering(
+ target.at("numbering"),
+ ..target.at("counter").at(target.location()))
+
+ // NOTE there's a nonbreaking space in the block below
+ link(target.location(), [#parent_figure.at("supplement") #counters#subcounter])
+ } else {
+ it
+ }
+})
+
+// 2023-10-09: #fa-icon("fa-info") is not working, so we'll eval "#fa-info()" instead
+#let callout(body: [], title: "Callout", background_color: rgb("#dddddd"), icon: none, icon_color: black) = {
+ block(
+ breakable: false,
+ fill: background_color,
+ stroke: (paint: icon_color, thickness: 0.5pt, cap: "round"),
+ width: 100%,
+ radius: 2pt,
+ block(
+ inset: 1pt,
+ width: 100%,
+ below: 0pt,
+ block(
+ fill: background_color,
+ width: 100%,
+ inset: 8pt)[#text(icon_color, weight: 900)[#icon] #title]) +
+ block(
+ inset: 1pt,
+ width: 100%,
+ block(fill: white, width: 100%, inset: 8pt, body)))
+}
+
+
+
+#let article(
+ title: none,
+ authors: none,
+ date: none,
+ abstract: none,
+ cols: 1,
+ margin: (x: 1.25in, y: 1.25in),
+ paper: "us-letter",
+ lang: "en",
+ region: "US",
+ font: (),
+ fontsize: 11pt,
+ sectionnumbering: none,
+ toc: false,
+ toc_title: none,
+ toc_depth: none,
+ doc,
+) = {
+ set page(
+ paper: paper,
+ margin: margin,
+ numbering: "1",
+ )
+ set par(justify: true)
+ set text(lang: lang,
+ region: region,
+ font: font,
+ size: fontsize)
+ set heading(numbering: sectionnumbering)
+
+ if title != none {
+ align(center)[#block(inset: 2em)[
+ #text(weight: "bold", size: 1.5em)[#title]
+ ]]
+ }
+
+ if authors != none {
+ let count = authors.len()
+ let ncols = calc.min(count, 3)
+ grid(
+ columns: (1fr,) * ncols,
+ row-gutter: 1.5em,
+ ..authors.map(author =>
+ align(center)[
+ #author.name \
+ #author.affiliation \
+ #author.email
+ ]
+ )
+ )
+ }
+
+ if date != none {
+ align(center)[#block(inset: 1em)[
+ #date
+ ]]
+ }
+
+ if abstract != none {
+ block(inset: 2em)[
+ #text(weight: "semibold")[Abstract] #h(1em) #abstract
+ ]
+ }
+
+ if toc {
+ let title = if toc_title == none {
+ auto
+ } else {
+ toc_title
+ }
+ block(above: 0em, below: 2em)[
+ #outline(
+ title: toc_title,
+ depth: toc_depth
+ );
+ ]
+ }
+
+ if cols == 1 {
+ doc
+ } else {
+ columns(cols, doc)
+ }
+}
+#show: doc => article(
+ title: [Assessment of graph literacy among German medical students – a cross-sectional study to assess graph interpretation skills],
+ authors: (
+ ( name: [Janina Soler Wenglein],
+ affiliation: [Universität Bielefeld, Medizinische Fakultät OWL],
+ email: [] ),
+ ( name: [Hendrik Friederichs],
+ affiliation: [Universität Bielefeld, Medizinische Fakultät OWL],
+ email: [hendrik.friederichs\@uni-bielefeld.de] ),
+ ),
+ date: [2024-01-23],
+ lang: "de",
+ abstract: [#strong[Background / Hintergrund];: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis posuere ligula sit amet lacinia. Duis dignissim pellentesque magna, rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit, non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim, at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.
+
+#strong[Methods / Methoden];: …
+
+#strong[Results / Ergebnisse];: …
+
+#strong[Conclusio / Schlussfolgerungen];: …
+
+],
+ toc_title: [Inhaltsverzeichnis],
+ toc_depth: 3,
+ cols: 1,
+ doc,
+)
+#import "@preview/fontawesome:0.1.0": *
+
+
+#super[1] Universität Bielefeld, Medizinische Fakultät OWL
+
+#super[✉] Correspondence: #link("mailto:hendrik.friederichs@uni-bielefeld.de")[Hendrik Friederichs \<hendrik.friederichs\@uni-bielefeld.de\>]
+
+#block[
+#callout(
+body:
+[
+This manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.
+
+#emph[Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal …]
+
+]
+,
+title:
+[
+IN PROGRESS …
+]
+,
+background_color:
+rgb("#ffe5d0")
+,
+icon_color:
+rgb("#FC5300")
+,
+icon:
+fa-fire()
+,
+)
+]
+#block[
+#callout(
+body:
+[
+#strong[Relevantes Problem:] Gesundheitskompetenz beruht auf verschiedenen Fähigkeiten zur Informationsverarbeitung. Sie ist für Ärzte essentiell, um medizinische Berichte, Behandlungen und Studienergebnisse zu verstehen, Patienten aufzuklären, klinische Entscheidungen zu treffen und effektiv im medizinischen Team zu kommunizieren. \
+#strong[Fokussiertes Problem:] Graph Literacy, als Teil der Gesundheitskompetenz, ist wichtig für das Lesen und Verstehen von Grafiken. Es umfasst das Entziffern von Zeichen und Symbolen und ist eng mit anderen Formen der Literacy verbunden. Dies ist besonders relevant in Bereichen wie der medizinischen Forschung, wo Grafiken zur Vermittlung komplexer Informationen genutzt werden. Fehldarstellungen können jedoch die Informationsaufnahme beeinflussen. \
+#strong[Gap des Problems:] Graph Literacy beeinflusst das Risikoverständnis und Entscheidungsverhalten, wobei Studien hauptsächlich Patienten und Ärzte im Umgang mit grafischen Darstellungen betrachten. \
+#strong[Lösung?:] Für Ärzte, die Menschen mit geringerer Gesundheitskompetenz betreuen, ist es wichtig, selbst eine hohe Kompetenz im Verständnis visueller Darstellungen zu haben. Dies beeinflusst die Entscheidungsfindung der Patienten und Ärzte. \
+#strong[Forschungsfragen:] Daher wurde eine Studie mit Medizinstudierenden durchgeführt, um ihre Fähigkeit zur Interpretation visuell dargestellter medizinischer Informationen zu untersuchen. \
+#strong[Studienpopulation:] Medizinstudierende \
+#strong[Studiendesign:] Beobachtungsstudie \
+#strong[Datenerhebung:] Graph Literacy Scale \
+#strong[Ergebnisparameter:] Anzahl der richtigen Antworten insgesamt. \
+#strong[Statistik:] Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen im Studienverlauf.
+
+]
+,
+title:
+[
+STRUKTUR DES MANUSKRIPTS
+]
+,
+background_color:
+rgb("#ccf1e3")
+,
+icon_color:
+rgb("#00A047")
+,
+icon:
+fa-lightbulb()
+,
+)
+]
+== Background
+<background>
+=== Broad problem
+<broad-problem>
+Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.
+
+=== Theoretical and/or empirical focus of the problem
+<theoretical-andor-empirical-focus-of-the-problem>
+One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.
+
+Processing those visual representations is essential for understanding scientific and statistical data \[1\] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others \(Cooper et al, 2002). But misleading representations \(either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient \(Melnik-Leroy, 2023).
+
+In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy \(Roth, 2002). It can have an impact on risk comprehension \(Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients \(Durand et al, 2020) or the ability of doctors \(Caverly et al, 2015) to interpret graphical representations.
+
+=== Focused problem statement
+<focused-problem-statement>
+Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for \(future) medical doctors.
+
+When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.
+
+=== Statement of study intent
+<statement-of-study-intent>
+We performed a study of medical students to investigate the following questions:
+
++ What is …
++ Why are …
+
+== Methods
+<methods>
+=== Setting and subjects
+<setting-and-subjects>
+Our study was conducted at Medical Faculty of Münster, Germany. It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section \(the first two years) and a clinical section \(the last four years). To improve students’ clinical experience, they are rotated in various hospital departments during their final year \("clinical/practical" year).
+
+=== Study design
+<study-design>
+The participants were asked to complete the graph literacy scale voluntarily and anonymously.
+
+=== Ethical approval
+<ethical-approval>
+Vestibulum ultrices, tortor at mattis porta, odio nisi rutrum nulla, sit amet tincidunt eros quam facilisis tellus. Fusce eleifend lectus in elementum lacinia. Nam auctor nunc in massa ullamcorper, sit amet auctor ante accumsan. Nam ut varius metus. Curabitur eget tristique leo. Cras finibus euismod erat eget elementum. Integer vel placerat ex. Ut id eros quis lectus lacinia venenatis hendrerit vel ante.
+
+=== Data collection
+<data-collection>
+Data collection for this study was determined à priori as follows:
+
+…
+
+```{webr-r}
+#| context: setup
+
+# Download a dataset
+download.file(
+ 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
+ 'penguins.csv'
+)
+
+# Read the data
+df_penguins = read.csv("penguins.csv")
+```
+
+=== Outcome Measures
+<outcome-measures>
+…
+
+=== Statistical methods
+<statistical-methods>
+…
+
+```{webr-r}
+#| context: interactive
+
+# Download a dataset
+download.file(
+ 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
+ 'penguins.csv'
+) # Download the dataset
+
+# Read the data
+penguins = read.csv("penguins.csv") # Read the data
+
+# Scatterplot example: penguin bill length versus bill depth
+ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # Build a scatterplot
+ ggplot2::geom_point(ggplot2::aes(color = species,
+ shape = species),
+ size = 2) +
+ ggplot2::scale_color_manual(values = c("darkorange","darkorchid","cyan4"))
+```
+
+== Results
+<results>
+=== Recruitment Process and Demographic Characteristics
+<recruitment-process-and-demographic-characteristics>
+The recruitment process is shown in Figure 1. We obtained XX complete data sets \(return rate YY.Z%) after contacting …
+
+=== Primary and secondary Outcomes
+<primary-and-secondary-outcomes>
+#figure([
+#box(width: 1682.0pt, image("Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg"))
+], caption: figure.caption(
+position: bottom,
+[
+Beispielgrafik: ein Bild sagt mehr als tausend Worte …
+]),
+kind: "quarto-float-fig",
+supplement: "Figure",
+)
+
+
+== Discussion
+<discussion>
+=== Summary
+<summary>
+After the evaluation of all datasets, the following findings emerged. The first is that …
+
+=== Limitation: study population
+<limitation-study-population>
+Duis urna urna, pellentesque eu urna ut, malesuada bibendum dolor. Suspendisse potenti. Vivamus ornare, arcu quis molestie ultrices, magna est accumsan augue, auctor vulputate erat quam quis neque. Nullam scelerisque odio vel ultricies facilisis. Ut porta arcu non magna sagittis lacinia. Cras ornare vulputate lectus a tristique. Pellentesque ac arcu congue, rhoncus mi id, dignissim ligula.
+
+=== Limitation: study design
+<limitation-study-design>
+…
+
+=== Integration with prior work
+<integration-with-prior-work>
+Only a few studies provide insights into the graphical and numerical skills among medical students.
+
+In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales’ mean scores were high among the medical students in this sample \[2\].
+
+=== Implications for practice
+<implications-for-practice>
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis sagittis posuere ligula sit amet lacinia. Duis dignissim pellentesque magna, rhoncus congue sapien finibus mollis. Ut eu sem laoreet, vehicula ipsum in, convallis erat. Vestibulum magna sem, blandit pulvinar augue sit amet, auctor malesuada sapien. Nullam faucibus leo eget eros hendrerit, non laoreet ipsum lacinia. Curabitur cursus diam elit, non tempus ante volutpat a. Quisque hendrerit blandit purus non fringilla. Integer sit amet elit viverra ante dapibus semper. Vestibulum viverra rutrum enim, at luctus enim posuere eu. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.
+
+=== Implications for research
+<implications-for-research>
+…
+
+=== Conclusions
+<conclusions>
+…
+
+#block[
+#heading(
+level:
+2
+,
+numbering:
+none
+,
+[
+References
+]
+)
+]
+#block[
+#block[
+1. Friel SN, Curcio FR, Bright GW. Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in mathematics Education. 2001;32:124–58.
+
+] <ref-friel2001making>
+#block[
+2. Mas G, Tello T, Ortiz P, Petrova D, GarcıÌa-Retamero R. Graphical and numerical skills in pre-and postgraduate medical students from a private university. Gac Med Mex. 2018;154:163–9.
+
+] <ref-mas2018graphical>
+] <refs>
+== Declarations
+<declarations>
+=== Ethics approval and consent to participate
+<ethics-approval-and-consent-to-participate>
+Vestibulum ultrices, tortor at mattis porta, odio nisi rutrum nulla, sit amet tincidunt eros quam facilisis tellus. Fusce eleifend lectus in elementum lacinia. Nam auctor nunc in massa ullamcorper, sit amet auctor ante accumsan. Nam ut varius metus. Curabitur eget tristique leo. Cras finibus euismod erat eget elementum. Integer vel placerat ex. Ut id eros quis lectus lacinia venenatis hendrerit vel ante.
+
+=== Consent for publication
+<consent-for-publication>
+Not applicable
+
+=== Availability of data and materials
+<availability-of-data-and-materials>
+The original data that support the findings of this study are available from Open Science Framework \(osf.io, see manuscript-URL).
+
+=== Competing interests
+<competing-interests>
+The authors declare that they have no competing interests.
+
+=== Funding
+<funding>
+The author\(s) received no specific funding for this work.
+
+=== Authors’ contributions
+<authors-contributions>
+Etiam congue quam eget velit convallis, eu sagittis orci vestibulum. Vestibulum at massa turpis. Curabitur ornare ex sed purus vulputate, vitae porta augue rhoncus. Phasellus auctor suscipit purus, vel ultricies nunc. Nunc eleifend nulla ac purus volutpat, id fringilla felis aliquet. Duis vitae porttitor nibh, in rhoncus risus. Vestibulum a est vitae est tristique vehicula. Proin mollis justo id est tempus hendrerit. Praesent suscipit placerat congue. Aliquam eu elit gravida, consequat augue non, ultricies sapien. Nunc ultricies viverra ante, sit amet vehicula ante volutpat id. Etiam tempus purus vitae tellus mollis viverra. Donec at ornare mauris. Aliquam sodales hendrerit ornare. Suspendisse accumsan lacinia sapien, sit amet imperdiet dui molestie ut.
+
+=== CRediT authorship contribution statement
+<credit-authorship-contribution-statement>
+#strong[Janina Soler Wenglein:] Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft. \
+#strong[Hendrik Friederichs:] Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing - review & editing, Writing - original draft.
+
+=== Acknowledgments
+<acknowledgments>
+The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.
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- <h1 class="title">Assessment of graph literacy among German medical students – a cross-sectional study to assess graph interpretation skills</h1>
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- <p class="author">Janina Soler Wenglein, MD <a href="https://orcid.org/0000-2222-1111-3333" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
- </div>
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- <p class="affiliation">
- <a href="https://www.uni-bielefeld.de/fakultaeten/medizin/">
- Universität Bielefeld, Medizinische Fakultät OWL
- </a>
- </p>
- </div>
- <div class="quarto-title-meta-contents">
- <p class="author"><a href="https://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=251340451">Hendrik Friederichs, MD</a> <a href="mailto:hendrik.friederichs@uni-bielefeld.de" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0001-9671-5235" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
- </div>
- <div class="quarto-title-meta-contents">
- <p class="affiliation">
- <a href="https://www.uni-bielefeld.de/fakultaeten/medizin/">
- Universität Bielefeld, Medizinische Fakultät OWL
- </a>
- </p>
- </div>
- </div>
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- <div class="quarto-title-meta">
-
-
- <div>
- <div class="quarto-title-meta-heading">Veröffentlichungsdatum</div>
- <div class="quarto-title-meta-contents">
- <p class="date">23. Januar 2024</p>
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- <div>
- <div class="abstract">
- <div class="block-title">Zusammenfassung</div>
- <p><strong>Background / Hintergrund</strong>: …</p>
- <p><strong>Methods / Methoden</strong>: …</p>
- <p><strong>Results / Ergebnisse</strong>: …</p>
- <p><strong>Conclusio / Schlussfolgerungen</strong>: …</p>
- </div>
- </div>
-
- <div>
- <div class="keywords">
- <div class="block-title">Schlüsselwörter</div>
- <p>Medical Education, Artificial Intelligence</p>
- </div>
- </div>
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- <h2 id="toc-title">Inhaltsverzeichnis</h2>
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- <li><a href="#background" id="toc-background" class="nav-link active" data-scroll-target="#background">Background</a>
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- <li><a href="#broad-problem" id="toc-broad-problem" class="nav-link" data-scroll-target="#broad-problem">Broad problem</a></li>
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- <li><a href="#study-design-studiendesign" id="toc-study-design-studiendesign" class="nav-link" data-scroll-target="#study-design-studiendesign">Study design / Studiendesign</a></li>
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- <li><a href="#outcome-measures-ergebnisparameter" id="toc-outcome-measures-ergebnisparameter" class="nav-link" data-scroll-target="#outcome-measures-ergebnisparameter">Outcome Measures / Ergebnisparameter</a></li>
- <li><a href="#statistical-methods-statistische-methoden" id="toc-statistical-methods-statistische-methoden" class="nav-link" data-scroll-target="#statistical-methods-statistische-methoden">Statistical methods / Statistische Methoden</a></li>
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- <ul class="collapse">
- <li><a href="#recruitment-process-and-demographic-characteristics-studienteilnahme" id="toc-recruitment-process-and-demographic-characteristics-studienteilnahme" class="nav-link" data-scroll-target="#recruitment-process-and-demographic-characteristics-studienteilnahme">Recruitment Process and Demographic Characteristics / Studienteilnahme</a></li>
- <li><a href="#primary-and-secondary-outcomes-haupt--und-nebenergebnisse" id="toc-primary-and-secondary-outcomes-haupt--und-nebenergebnisse" class="nav-link" data-scroll-target="#primary-and-secondary-outcomes-haupt--und-nebenergebnisse">Primary and secondary Outcomes / Haupt- und Nebenergebnisse</a></li>
- </ul></li>
- <li><a href="#discussion-diskussion" id="toc-discussion-diskussion" class="nav-link" data-scroll-target="#discussion-diskussion">Discussion / Diskussion</a>
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- <li><a href="#summary-zusammenfassung-der-ergebnisse" id="toc-summary-zusammenfassung-der-ergebnisse" class="nav-link" data-scroll-target="#summary-zusammenfassung-der-ergebnisse">Summary / Zusammenfassung der Ergebnisse</a></li>
- <li><a href="#limitation-study-population" id="toc-limitation-study-population" class="nav-link" data-scroll-target="#limitation-study-population">Limitation: study population</a></li>
- <li><a href="#limitation-study-ndesign" id="toc-limitation-study-ndesign" class="nav-link" data-scroll-target="#limitation-study-ndesign">Limitation: study ndesign</a></li>
- <li><a href="#integration-with-prior-work" id="toc-integration-with-prior-work" class="nav-link" data-scroll-target="#integration-with-prior-work">Integration with prior work</a></li>
- <li><a href="#implications-for-practice" id="toc-implications-for-practice" class="nav-link" data-scroll-target="#implications-for-practice">Implications for practice</a></li>
- <li><a href="#implications-for-research" id="toc-implications-for-research" class="nav-link" data-scroll-target="#implications-for-research">Implications for research</a></li>
- </ul></li>
- <li><a href="#conclusions" id="toc-conclusions" class="nav-link" data-scroll-target="#conclusions">Conclusions</a></li>
- <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
- <li><a href="#declarations" id="toc-declarations" class="nav-link" data-scroll-target="#declarations">Declarations</a>
- <ul class="collapse">
- <li><a href="#ethics-approval-and-consent-to-participate" id="toc-ethics-approval-and-consent-to-participate" class="nav-link" data-scroll-target="#ethics-approval-and-consent-to-participate">Ethics approval and consent to participate</a></li>
- <li><a href="#consent-for-publication" id="toc-consent-for-publication" class="nav-link" data-scroll-target="#consent-for-publication">Consent for publication</a></li>
- <li><a href="#availability-of-data-and-materials" id="toc-availability-of-data-and-materials" class="nav-link" data-scroll-target="#availability-of-data-and-materials">Availability of data and materials</a></li>
- <li><a href="#competing-interests-konkurrierende-interessen" id="toc-competing-interests-konkurrierende-interessen" class="nav-link" data-scroll-target="#competing-interests-konkurrierende-interessen">Competing interests / Konkurrierende Interessen</a></li>
- <li><a href="#funding-finanzierung" id="toc-funding-finanzierung" class="nav-link" data-scroll-target="#funding-finanzierung">Funding / Finanzierung</a></li>
- <li><a href="#authors-contributions-beiträge-der-autorinnen" id="toc-authors-contributions-beiträge-der-autorinnen" class="nav-link" data-scroll-target="#authors-contributions-beiträge-der-autorinnen">Authors’ contributions / Beiträge der Autor*innen</a></li>
- <li><a href="#credit-authorship-contribution-statement" id="toc-credit-authorship-contribution-statement" class="nav-link" data-scroll-target="#credit-authorship-contribution-statement">CRediT authorship contribution statement</a></li>
- <li><a href="#acknowledgments-danksagung" id="toc-acknowledgments-danksagung" class="nav-link" data-scroll-target="#acknowledgments-danksagung">Acknowledgments / Danksagung</a></li>
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- <p><sup>1</sup> Universität Bielefeld, Medizinische Fakultät OWL</p>
-<p><sup>✉</sup> Correspondence: <a href="mailto:hendrik.friederichs@uni-bielefeld.de">Hendrik Friederichs <hendrik.friederichs@uni-bielefeld.de></a></p>
-<div class="callout callout-style-default callout-caution callout-titled" title="IN PROGRESS ...">
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-<p>This manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.</p>
-<p><span style="color: grey;"><em>Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal …</em></span></p>
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-<p><span style="color: grey;"><strong>Relevantes Problem:</strong> Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant.<br>
-<strong>Fokussiertes Problem:</strong> Studienlage zu THEMA allgemein und Medical-Education-Kontext;<br>
-Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus.<br>
-<strong>Gap des Problems:</strong> <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend.<br>
-<strong>Lösung?:</strong> <!-- möglicher Fortschritt / mögliche Lösung - Ansprechen von Motiv(en): Anschluss, Leistung, Macht --> Gibt es einen Fortschritt durch bessere Leistungen der neuen Möglichkeiten?<br>
-<strong>Forschungsfragen:</strong> Wie ist die absolute Leistung von THEMA im Progress Test Medizin?<br>
-Wie ist die relative Leistung im Vergleich zu Medizinstudierenden?<br>
-Wie sieht die Leistung bei detaillierter Betrachtung der Domänen und Kompetenzlevel aus?<br>
-<strong>Studienpopulation:</strong> Medizinstudierende<br>
-<strong>Studiendesign:</strong> Kontrollierte Studie<br>
-<strong>Datenerhebung:</strong> 200 Multiple-Choice-Fragen aus dem Progress Test Medizin<br>
-<strong>Ergebnisparameter:</strong> Anzahl der richtigen Antworten insgesamt und pro Domäne bzw. Kompetenzlevel<br>
-<strong>Statistik:</strong> Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen.</span></p>
-</div>
-</div>
-</div>
-<section id="background" class="level2">
-<h2 class="anchored" data-anchor-id="background">Background</h2>
-<section id="broad-problem" class="level3">
-<h3 class="anchored" data-anchor-id="broad-problem">Broad problem</h3>
-<p>Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.</p>
-</section>
-<section id="theoretical-andor-empirical-focus-of-the-problem" class="level3">
-<h3 class="anchored" data-anchor-id="theoretical-andor-empirical-focus-of-the-problem">Theoretical and/or empirical focus of the problem</h3>
-<p>One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.</p>
-<p>Processing those visual representations is essential for understanding scientific and statistical data <span class="citation" data-cites="friel2001making">[<a href="#ref-friel2001making" role="doc-biblioref">1</a>]</span> and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).</p>
-<div class="no-row-height"><div id="ref-friel2001making" class="csl-entry" role="listitem">
-1. Friel SN, Curcio FR, Bright GW. Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in mathematics Education. 2001;32:124–58.
-</div></div><p>In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.</p>
-</section>
-<section id="focused-problem-statement" class="level3">
-<h3 class="anchored" data-anchor-id="focused-problem-statement">Focused problem statement</h3>
-<p>Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.</p>
-<p>When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.</p>
-</section>
-<section id="statement-of-study-intent" class="level3">
-<h3 class="anchored" data-anchor-id="statement-of-study-intent">Statement of study intent</h3>
-<p>We performed a study of medical students to investigate the following questions:</p>
-<ol type="1">
-<li>What is …</li>
-<li>Why are …</li>
-</ol>
-</section>
-</section>
-<section id="methods" class="level2">
-<h2 class="anchored" data-anchor-id="methods">Methods</h2>
-<section id="setting-and-subjects" class="level3">
-<h3 class="anchored" data-anchor-id="setting-and-subjects">Setting and subjects</h3>
-<p>Our study was conducted at Medical Faculty of Münster …</p>
-<p>It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students’ clinical experience, they are rotated in various hospital departments during their final year (“clinical/practical†year). …</p>
-</section>
-<section id="study-design-studiendesign" class="level3">
-<h3 class="anchored" data-anchor-id="study-design-studiendesign">Study design / Studiendesign</h3>
-<p>The participants were asked to complete the graph literacy scale voluntarily and anonymously.</p>
-</section>
-<section id="ethical-approval" class="level3">
-<h3 class="anchored" data-anchor-id="ethical-approval">Ethical approval</h3>
-<p>All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).</p>
-</section>
-<section id="data-collection" class="level3">
-<h3 class="anchored" data-anchor-id="data-collection">Data collection</h3>
-<p>Data collection for this study was determined à priori as follows:</p>
-<ul>
-<li>Input …</li>
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-<section id="outcome-measures-ergebnisparameter" class="level3">
-<h3 class="anchored" data-anchor-id="outcome-measures-ergebnisparameter">Outcome Measures / Ergebnisparameter</h3>
-<p>…</p>
-</section>
-<section id="statistical-methods-statistische-methoden" class="level3">
-<h3 class="anchored" data-anchor-id="statistical-methods-statistische-methoden">Statistical methods / Statistische Methoden</h3>
-<p>We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) <span class="citation" data-cites="hattie2023visible">[<a href="#ref-hattie2023visible" role="doc-biblioref">2</a>]</span> in outcome level between intervention and control groups (calculated <em>a priori</em> with G*Power 3.1) <span class="citation" data-cites="faul2007g">[<a href="#ref-faul2007g" role="doc-biblioref">3</a>]</span>. Statistical analysis, tables and figures were conducted using R <span class="citation" data-cites="R-base">[<a href="#ref-R-base" role="doc-biblioref">4</a>]</span> in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages <span class="citation" data-cites="tidyverse gt patil2021visualizations">[<a href="#ref-tidyverse" role="doc-biblioref">5</a>–<a href="#ref-patil2021visualizations" role="doc-biblioref">7</a>]</span>. Descriptive means and standard deviations were calculated for participants’ age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. …</p>
-<div class="no-row-height"><div id="ref-hattie2023visible" class="csl-entry" role="listitem">
-2. Hattie J. Visible learning: The sequel: A synthesis of over 2,100 meta-analyses relating to achievement. Taylor & Francis; 2023.
-</div><div id="ref-faul2007g" class="csl-entry" role="listitem">
-3. Faul F, Erdfelder E, Lang A-G, Buchner A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods. 2007;39:175–91.
-</div><div id="ref-R-base" class="csl-entry" role="listitem">
-4. R Core Team. <a href="https://www.R-project.org">R: A Language and Environment for Statistical Computing</a>. Vienna, Austria: R Foundation for Statistical Computing; 2019.
-</div><div id="ref-tidyverse" class="csl-entry" role="listitem">
-5. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, u. a. <a href="https://doi.org/10.21105/joss.01686">Welcome to the <span></span>tidyverse<span></span></a>. 2019;4:1686.
-</div><div id="ref-patil2021visualizations" class="csl-entry" role="listitem">
-7. Patil I. Visualizations with statistical details: The’ggstatsplot’approach. Journal of Open Source Software. 2021;6:3167.
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-<ol type="1">
-<li>Download the dataset</li>
-<li>Read the data</li>
-<li>Build a scatterplot</li>
-</ol>
-</section>
-</section>
-<section id="results-ergebnisse" class="level2">
-<h2 class="anchored" data-anchor-id="results-ergebnisse">Results / Ergebnisse</h2>
-<section id="recruitment-process-and-demographic-characteristics-studienteilnahme" class="level3">
-<h3 class="anchored" data-anchor-id="recruitment-process-and-demographic-characteristics-studienteilnahme">Recruitment Process and Demographic Characteristics / Studienteilnahme</h3>
-<p>The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …</p>
-<!-- Man kann Code-Ergebnisse über einfügen -->
-</section>
-<section id="primary-and-secondary-outcomes-haupt--und-nebenergebnisse" class="level3">
-<h3 class="anchored" data-anchor-id="primary-and-secondary-outcomes-haupt--und-nebenergebnisse">Primary and secondary Outcomes / Haupt- und Nebenergebnisse</h3>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><a href="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="lightbox" data-glightbox="description: .lightbox-desc-1" data-gallery="quarto-lightbox-gallery-1" title="Beispielgrafik: ein Bild sagt mehr als tausend Worte …"><img src="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="img-fluid figure-img" alt="Beispielgrafik: ein Bild sagt mehr als tausend Worte …"></a></p>
-<figcaption>Beispielgrafik: ein Bild sagt mehr als tausend Worte …</figcaption>
-</figure>
-</div>
-<!-- Man kann Code-Ergebnisse über einfügen -->
-</section>
-</section>
-<section id="discussion-diskussion" class="level2">
-<h2 class="anchored" data-anchor-id="discussion-diskussion">Discussion / Diskussion</h2>
-<section id="summary-zusammenfassung-der-ergebnisse" class="level3">
-<h3 class="anchored" data-anchor-id="summary-zusammenfassung-der-ergebnisse">Summary / Zusammenfassung der Ergebnisse</h3>
-<p>After the evaluation of all datasets, the following findings emerged. The first is that …</p>
-</section>
-<section id="limitation-study-population" class="level3">
-<h3 class="anchored" data-anchor-id="limitation-study-population">Limitation: study population</h3>
-</section>
-<section id="limitation-study-ndesign" class="level3">
-<h3 class="anchored" data-anchor-id="limitation-study-ndesign">Limitation: study ndesign</h3>
-</section>
-<section id="integration-with-prior-work" class="level3">
-<h3 class="anchored" data-anchor-id="integration-with-prior-work">Integration with prior work</h3>
-<p>…</p>
-<p>Only a few studies provide insights into the graphical and numerical skills among medical students.</p>
-<p>In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales’ mean scores were high among the medical students in this sample <span class="citation" data-cites="mas2018graphical">[<a href="#ref-mas2018graphical" role="doc-biblioref">8</a>]</span>.</p>
-<div class="no-row-height"><div id="ref-mas2018graphical" class="csl-entry" role="listitem">
-8. Mas G, Tello T, Ortiz P, Petrova D, GarcıÌa-Retamero R. Graphical and numerical skills in pre-and postgraduate medical students from a private university. Gac Med Mex. 2018;154:163–9.
-</div></div></section>
-<section id="implications-for-practice" class="level3">
-<h3 class="anchored" data-anchor-id="implications-for-practice">Implications for practice</h3>
-<p>…</p>
-</section>
-<section id="implications-for-research" class="level3">
-<h3 class="anchored" data-anchor-id="implications-for-research">Implications for research</h3>
-<p>…</p>
-</section>
-</section>
-<section id="conclusions" class="level2">
-<h2 class="anchored" data-anchor-id="conclusions">Conclusions</h2>
-<p>…</p>
-</section>
-<section id="references" class="level2 unnumbered">
-<h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
-
-</section>
-<section id="declarations" class="level2 appendix">
-<h2 class="appendix anchored" data-anchor-id="declarations">Declarations</h2>
-<section id="ethics-approval-and-consent-to-participate" class="level3">
-<h3 class="anchored" data-anchor-id="ethics-approval-and-consent-to-participate">Ethics approval and consent to participate</h3>
-<p>Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).</p>
-</section>
-<section id="consent-for-publication" class="level3">
-<h3 class="anchored" data-anchor-id="consent-for-publication">Consent for publication</h3>
-<p>Not applicable</p>
-</section>
-<section id="availability-of-data-and-materials" class="level3">
-<h3 class="anchored" data-anchor-id="availability-of-data-and-materials">Availability of data and materials</h3>
-<p>The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).</p>
-</section>
-<section id="competing-interests-konkurrierende-interessen" class="level3">
-<h3 class="anchored" data-anchor-id="competing-interests-konkurrierende-interessen">Competing interests / Konkurrierende Interessen</h3>
-<p>The authors declare that they have no competing interests.</p>
-</section>
-<section id="funding-finanzierung" class="level3">
-<h3 class="anchored" data-anchor-id="funding-finanzierung">Funding / Finanzierung</h3>
-<p>The author(s) received no specific funding for this work.</p>
-</section>
-<section id="authors-contributions-beiträge-der-autorinnen" class="level3">
-<h3 class="anchored" data-anchor-id="authors-contributions-beiträge-der-autorinnen">Authors’ contributions / Beiträge der Autor*innen</h3>
-<p>HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.</p>
-</section>
-<section id="credit-authorship-contribution-statement" class="level3">
-<h3 class="anchored" data-anchor-id="credit-authorship-contribution-statement">CRediT authorship contribution statement</h3>
-<p><strong>Janina Soler Wenglein:</strong> Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. <strong>Hendrik Friederichs:</strong> Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.</p>
-</section>
-<section id="acknowledgments-danksagung" class="level3">
-<h3 class="anchored" data-anchor-id="acknowledgments-danksagung">Acknowledgments / Danksagung</h3>
-<p>The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.</p>
-<div class="hidden" aria-hidden="true">
-<span class="glightbox-desc lightbox-desc-1">Beispielgrafik: ein Bild sagt mehr als tausend Worte …</span>
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diff --git a/public/index.embed.ipynb b/public/index.embed.ipynb
deleted file mode 100644
index 838573983396fb3d6dc051c7059fd96684330f93..0000000000000000000000000000000000000000
--- a/public/index.embed.ipynb
+++ /dev/null
@@ -1,272 +0,0 @@
-{
- "cells": [
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- "source": [
- "# Assessment of graph literacy among German medical students – a\n",
- "\n",
- "cross-sectional study to assess graph interpretation skills\n",
- "\n",
- "Draft of the manuscript\n",
- "\n",
- "Janina Soler Wenglein [](https://orcid.org/0000-2222-1111-3333) ([Universität Bielefeld, Medizinische Fakultät OWL](https://www.uni-bielefeld.de/fakultaeten/medizin/)) \n",
- "[Hendrik Friederichs](https://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=251340451) [](https://orcid.org/0000-0001-9671-5235) ([Universität Bielefeld, Medizinische Fakultät OWL](https://www.uni-bielefeld.de/fakultaeten/medizin/)) \n",
- "23. Januar 2024\n",
- "\n",
- "**Background / Hintergrund**: …\n",
- "\n",
- "**Methods / Methoden**: …\n",
- "\n",
- "**Results / Ergebnisse**: …\n",
- "\n",
- "**Conclusio / Schlussfolgerungen**: …\n",
- "\n",
- "<sup>1</sup> Universität Bielefeld, Medizinische Fakultät OWL\n",
- "\n",
- "<sup>✉</sup> Correspondence: [Hendrik Friederichs \\<hendrik.friederichs@uni-bielefeld.de\\>](mailto:hendrik.friederichs@uni-bielefeld.de)\n",
- "\n",
- "> **IN PROGRESS …**\n",
- ">\n",
- "> This manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n",
- ">\n",
- "> <span color=\"grey\">*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal …*</span>\n",
- "\n",
- "> **STRUKTUR DES MANUSKRIPTS**\n",
- ">\n",
- "> <span color=\"grey\">**Relevantes Problem:** Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant. \n",
- "> **Fokussiertes Problem:** Studienlage zu THEMA allgemein und Medical-Education-Kontext; \n",
- "> Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus. \n",
- "> **Gap des Problems:** <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend. \n",
- "> **Lösung?:** <!-- möglicher Fortschritt / mögliche Lösung - Ansprechen von Motiv(en): Anschluss, Leistung, Macht --> Gibt es einen Fortschritt durch bessere Leistungen der neuen Möglichkeiten? \n",
- "> **Forschungsfragen:** Wie ist die absolute Leistung von THEMA im Progress Test Medizin? \n",
- "> Wie ist die relative Leistung im Vergleich zu Medizinstudierenden? \n",
- "> Wie sieht die Leistung bei detaillierter Betrachtung der Domänen und Kompetenzlevel aus? \n",
- "> **Studienpopulation:** Medizinstudierende \n",
- "> **Studiendesign:** Kontrollierte Studie \n",
- "> **Datenerhebung:** 200 Multiple-Choice-Fragen aus dem Progress Test Medizin \n",
- "> **Ergebnisparameter:** Anzahl der richtigen Antworten insgesamt und pro Domäne bzw. Kompetenzlevel \n",
- "> **Statistik:** Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen.</span>\n",
- "\n",
- "## Background\n",
- "\n",
- "### Broad problem\n",
- "\n",
- "Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n",
- "\n",
- "### Theoretical and/or empirical focus of the problem\n",
- "\n",
- "One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n",
- "\n",
- "Processing those visual representations is essential for understanding scientific and statistical data \\[@friel2001making\\] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n",
- "\n",
- "In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n",
- "\n",
- "### Focused problem statement\n",
- "\n",
- "Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n",
- "\n",
- "When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n",
- "\n",
- "### Statement of study intent\n",
- "\n",
- "We performed a study of medical students to investigate the following questions:\n",
- "\n",
- "1. What is …\n",
- "2. Why are …\n",
- "\n",
- "## Methods\n",
- "\n",
- "### Setting and subjects\n",
- "\n",
- "Our study was conducted at Medical Faculty of Münster …\n",
- "\n",
- "It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students’ clinical experience, they are rotated in various hospital departments during their final year (“clinical/practical†year). …\n",
- "\n",
- "### Study design / Studiendesign\n",
- "\n",
- "The participants were asked to complete the graph literacy scale voluntarily and anonymously.\n",
- "\n",
- "### Ethical approval\n",
- "\n",
- "All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
- "\n",
- "### Data collection\n",
- "\n",
- "Data collection for this study was determined à priori as follows:\n",
- "\n",
- "- Input …\n",
- "\n",
- "``` {webr-r}\n",
- "#| context: setup\n",
- "\n",
- "# Download a dataset\n",
- "download.file(\n",
- " 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n",
- " 'penguins.csv'\n",
- ")\n",
- "\n",
- "# Read the data\n",
- "df_penguins = read.csv(\"penguins.csv\")\n",
- "```\n",
- "\n",
- "### Outcome Measures / Ergebnisparameter\n",
- "\n",
- "…\n",
- "\n",
- "### Statistical methods / Statistische Methoden\n",
- "\n",
- "We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) \\[@hattie2023visible\\] in outcome level between intervention and control groups (calculated *a priori* with G\\*Power 3.1) \\[@faul2007g\\]. Statistical analysis, tables and figures were conducted using R \\[@R-base\\] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages \\[@tidyverse; @gt; @patil2021visualizations\\]. Descriptive means and standard deviations were calculated for participants’ age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. …\n",
- "\n",
- "``` {webr-r}\n",
- "#| context: interactive\n",
- "\n",
- "# Download a dataset\n",
- "download.file(\n",
- " 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n",
- " 'penguins.csv'\n",
- ")\n",
- "\n",
- "# Read the data\n",
- "penguins = read.csv(\"penguins.csv\")\n",
- "\n",
- "# Scatterplot example: penguin bill length versus bill depth\n",
- "ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) +\n",
- " ggplot2::geom_point(ggplot2::aes(color = species, \n",
- " shape = species),\n",
- " size = 2) +\n",
- " ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n",
- "```\n",
- "\n",
- "Zeile 7 \n",
- "Download the dataset\n",
- "\n",
- "Zeile 10 \n",
- "Read the data\n",
- "\n",
- "Zeilen 13,15,17 \n",
- "Build a scatterplot\n",
- "\n",
- "## Results / Ergebnisse\n",
- "\n",
- "### Recruitment Process and Demographic Characteristics / Studienteilnahme\n",
- "\n",
- "The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …"
- ],
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- },
- {
- "cell_type": "raw",
- "metadata": {
- "raw_mimetype": "text/html"
- },
- "source": [
- "<!-- Man kann Code-Ergebnisse über einfügen -->"
- ],
- "id": "413a72b7-1644-470f-ba9c-bf81f3b64da9"
- },
- {
- "cell_type": "markdown",
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- "source": [
- "### Primary and secondary Outcomes / Haupt- und Nebenergebnisse\n",
- "\n",
- "<figure>\n",
- "<img src=\"attachment:Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg\" alt=\"Beispielgrafik: ein Bild sagt mehr als tausend Worte …\" />\n",
- "<figcaption aria-hidden=\"true\">Beispielgrafik: ein Bild sagt mehr als tausend Worte …</figcaption>\n",
- "</figure>"
- ],
- "attachments": {
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3LGO7kbfevyZ1r/goB8YPiJ+2o/wi+EvjDwjoXw10TULRZb++urUR6nZRSwrd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\nUgv7VX/YS1z/ANPclfT+pf8ABPvxHpv7Xc37Unwy+Kd14Wj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A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\n+a1/KGvq2gArH1nW7PRLd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- "## Discussion / Diskussion\n",
- "\n",
- "### Summary / Zusammenfassung der Ergebnisse\n",
- "\n",
- "After the evaluation of all datasets, the following findings emerged. The first is that …\n",
- "\n",
- "### Limitation: study population\n",
- "\n",
- "### Limitation: study ndesign\n",
- "\n",
- "### Integration with prior work\n",
- "\n",
- "…\n",
- "\n",
- "Only a few studies provide insights into the graphical and numerical skills among medical students.\n",
- "\n",
- "In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales’ mean scores were high among the medical students in this sample \\[@mas2018graphical\\].\n",
- "\n",
- "### Implications for practice\n",
- "\n",
- "…\n",
- "\n",
- "### Implications for research\n",
- "\n",
- "…\n",
- "\n",
- "## Conclusions\n",
- "\n",
- "…\n",
- "\n",
- "## References\n",
- "\n",
- "## Declarations\n",
- "\n",
- "### Ethics approval and consent to participate\n",
- "\n",
- "Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
- "\n",
- "### Consent for publication\n",
- "\n",
- "Not applicable\n",
- "\n",
- "### Availability of data and materials\n",
- "\n",
- "The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n",
- "\n",
- "### Competing interests / Konkurrierende Interessen\n",
- "\n",
- "The authors declare that they have no competing interests.\n",
- "\n",
- "### Funding / Finanzierung\n",
- "\n",
- "The author(s) received no specific funding for this work.\n",
- "\n",
- "### Authors’ contributions / Beiträge der Autor\\*innen\n",
- "\n",
- "HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.\n",
- "\n",
- "### CRediT authorship contribution statement\n",
- "\n",
- "**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n",
- "\n",
- "### Acknowledgments / Danksagung\n",
- "\n",
- "The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility."
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<div class="abstract">
<div class="block-title">Zusammenfassung</div>
- <p><strong>Background / Hintergrund</strong>: Nunc ac dignissim magna. Vestibulum vitae egestas elit. Proin feugiat leo quis ante condimentum, eu ornare mauris feugiat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Mauris cursus laoreet ex, dignissim bibendum est posuere iaculis. Suspendisse et maximus elit. In fringilla gravida ornare. Aenean id lectus pulvinar, sagittis felis nec, rutrum risus. Nam vel neque eu arcu blandit fringilla et in quam. Aliquam luctus est sit amet vestibulum eleifend. Phasellus elementum sagittis molestie. Proin tempor lorem arcu, at condimentum purus volutpat eu. Fusce et pellentesque ligula. Pellentesque id tellus at erat luctus fringilla. Suspendisse potenti.</p>
+ <p><strong>Background / Hintergrund</strong>: Nullam dapibus cursus dolor sit amet consequat. Nulla facilisi. Curabitur vel nulla non magna lacinia tincidunt. Duis porttitor quam leo, et blandit velit efficitur ut. Etiam auctor tincidunt porttitor. Phasellus sed accumsan mi. Fusce ut erat dui. Suspendisse eu augue eget turpis condimentum finibus eu non lorem. Donec finibus eros eu ante condimentum, sed pharetra sapien sagittis. Phasellus non dolor ac ante mollis auctor nec et sapien. Pellentesque vulputate at nisi eu tincidunt. Vestibulum at dolor aliquam, hendrerit purus eu, eleifend massa. Morbi consectetur eros id tincidunt gravida. Fusce ut enim quis orci hendrerit lacinia sed vitae enim.</p>
<p><strong>Methods / Methoden</strong>: …</p>
<p><strong>Results / Ergebnisse</strong>: …</p>
<p><strong>Conclusio / Schlussfolgerungen</strong>: …</p>
@@ -446,7 +446,7 @@ STRUKTUR DES MANUSKRIPTS
</section>
<section id="ethical-approval" class="level3">
<h3 class="anchored" data-anchor-id="ethical-approval">Ethical approval</h3>
-<p>Nulla eget cursus ipsum. Vivamus porttitor leo diam, sed volutpat lectus facilisis sit amet. Maecenas et pulvinar metus. Ut at dignissim tellus. In in tincidunt elit. Etiam vulputate lobortis arcu, vel faucibus leo lobortis ac. Aliquam erat volutpat. In interdum orci ac est euismod euismod. Nunc eleifend tristique risus, at lacinia odio commodo in. Sed aliquet ligula odio, sed tempor neque ultricies sit amet.</p>
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</section>
<section id="data-collection" class="level3">
<h3 class="anchored" data-anchor-id="data-collection">Data collection</h3>
@@ -658,11 +658,6 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
executeCode(editor.getValue());
};
</script>
-<ol type="1">
-<li></li>
-<li></li>
-<li></li>
-</ol>
</section>
</section>
<section id="results" class="level2">
@@ -691,7 +686,7 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
</section>
<section id="limitation-study-population" class="level3">
<h3 class="anchored" data-anchor-id="limitation-study-population">Limitation: study population</h3>
-<p>Etiam non efficitur urna, quis elementum nisi. Mauris posuere a augue vel gravida. Praesent luctus erat et ex iaculis interdum. Nulla vestibulum quam ac nunc consequat vulputate. Nullam iaculis lobortis sem sit amet fringilla. Aliquam semper, metus ut blandit semper, nulla velit fermentum sapien, fermentum ultrices dolor sapien sed leo. Vestibulum molestie faucibus magna, at feugiat nulla ullamcorper a. Aliquam erat volutpat. Praesent scelerisque magna a justo maximus, sit amet suscipit mauris tempor. Nulla nec dolor eget ipsum pellentesque lobortis a in ipsum. Morbi turpis turpis, fringilla a eleifend maximus, viverra nec neque. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos.</p>
+<p>Etiam maximus accumsan gravida. Maecenas at nunc dignissim, euismod enim ac, bibendum ipsum. Maecenas vehicula velit in nisl aliquet ultricies. Nam eget massa interdum, maximus arcu vel, pretium erat. Maecenas sit amet tempor purus, vitae aliquet nunc. Vivamus cursus urna velit, eleifend dictum magna laoreet ut. Duis eu erat mollis, blandit magna id, tincidunt ipsum. Integer massa nibh, commodo eu ex vel, venenatis efficitur ligula. Integer convallis lacus elit, maximus eleifend lacus ornare ac. Vestibulum scelerisque viverra urna id lacinia. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Aenean eget enim at diam bibendum tincidunt eu non purus. Nullam id magna ultrices, sodales metus viverra, tempus turpis.</p>
</section>
<section id="limitation-study-design" class="level3">
<h3 class="anchored" data-anchor-id="limitation-study-design">Limitation: study design</h3>
@@ -706,7 +701,7 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
</div></div></section>
<section id="implications-for-practice" class="level3">
<h3 class="anchored" data-anchor-id="implications-for-practice">Implications for practice</h3>
-<p>Maecenas turpis velit, ultricies non elementum vel, luctus nec nunc. Nulla a diam interdum, faucibus sapien viverra, finibus metus. Donec non tortor diam. In ut elit aliquet, bibendum sem et, aliquam tortor. Donec congue, sem at rhoncus ultrices, nunc augue cursus erat, quis porttitor mauris libero ut ex. Nullam quis leo urna. Donec faucibus ligula eget pellentesque interdum. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean rhoncus interdum erat ut ultricies. Aenean tempus ex non elit suscipit, quis dignissim enim efficitur. Proin laoreet enim massa, vitae laoreet nulla mollis quis.</p>
+<p>Proin sodales neque erat, varius cursus diam tincidunt sit amet. Etiam scelerisque fringilla nisl eu venenatis. Donec sem ipsum, scelerisque ac venenatis quis, hendrerit vel mauris. Praesent semper erat sit amet purus condimentum, sit amet auctor mi feugiat. In hac habitasse platea dictumst. Nunc ac mauris in massa feugiat bibendum id in dui. Praesent accumsan urna at lacinia aliquet. Proin ultricies eu est quis pellentesque. In vel lorem at nisl rhoncus cursus eu quis mi. In eu rutrum ante, quis placerat justo. Etiam euismod nibh nibh, sed elementum nunc imperdiet in. Praesent gravida nunc vel odio lacinia, at tempus nisl placerat. Aenean id ipsum sed est sagittis hendrerit non in tortor.</p>
</section>
<section id="implications-for-research" class="level3">
<h3 class="anchored" data-anchor-id="implications-for-research">Implications for research</h3>
@@ -727,7 +722,7 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
<section id="ethics-approval-and-consent-to-participate" class="level3">
<h3 class="anchored" data-anchor-id="ethics-approval-and-consent-to-participate">Ethics approval and consent to participate</h3>
-<p>Duis urna urna, pellentesque eu urna ut, malesuada bibendum dolor. Suspendisse potenti. Vivamus ornare, arcu quis molestie ultrices, magna est accumsan augue, auctor vulputate erat quam quis neque. Nullam scelerisque odio vel ultricies facilisis. Ut porta arcu non magna sagittis lacinia. Cras ornare vulputate lectus a tristique. Pellentesque ac arcu congue, rhoncus mi id, dignissim ligula.</p>
+<p>Nulla eget cursus ipsum. Vivamus porttitor leo diam, sed volutpat lectus facilisis sit amet. Maecenas et pulvinar metus. Ut at dignissim tellus. In in tincidunt elit. Etiam vulputate lobortis arcu, vel faucibus leo lobortis ac. Aliquam erat volutpat. In interdum orci ac est euismod euismod. Nunc eleifend tristique risus, at lacinia odio commodo in. Sed aliquet ligula odio, sed tempor neque ultricies sit amet.</p>
</section>
<section id="consent-for-publication" class="level3">
<h3 class="anchored" data-anchor-id="consent-for-publication">Consent for publication</h3>
@@ -747,7 +742,7 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
</section>
<section id="authors-contributions" class="level3">
<h3 class="anchored" data-anchor-id="authors-contributions">Authors’ contributions</h3>
-<p>Duis urna urna, pellentesque eu urna ut, malesuada bibendum dolor. Suspendisse potenti. Vivamus ornare, arcu quis molestie ultrices, magna est accumsan augue, auctor vulputate erat quam quis neque. Nullam scelerisque odio vel ultricies facilisis. Ut porta arcu non magna sagittis lacinia. Cras ornare vulputate lectus a tristique. Pellentesque ac arcu congue, rhoncus mi id, dignissim ligula.</p>
+<p>Ut ut condimentum augue, nec eleifend nisl. Sed facilisis egestas odio ac pretium. Pellentesque consequat magna sed venenatis sagittis. Vivamus feugiat lobortis magna vitae accumsan. Pellentesque euismod malesuada hendrerit. Ut non mauris non arcu condimentum sodales vitae vitae dolor. Nullam dapibus, velit eget lacinia rutrum, ipsum justo malesuada odio, et lobortis sapien magna vel lacus. Nulla purus neque, hendrerit non malesuada eget, mattis vel erat. Suspendisse potenti.</p>
</section>
<section id="credit-authorship-contribution-statement" class="level3">
<h3 class="anchored" data-anchor-id="credit-authorship-contribution-statement">CRediT authorship contribution statement</h3>
@@ -1306,7 +1301,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
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</div> <!-- /content -->
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diff --git a/public/index.out.ipynb b/public/index.out.ipynb
deleted file mode 100644
index 8e38a62ba3e32bd8418f6eec86e79e243fe0c11d..0000000000000000000000000000000000000000
--- a/public/index.out.ipynb
+++ /dev/null
@@ -1,288 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Assessment of graph literacy among German medical students – a\n",
- "\n",
- "cross-sectional study to assess graph interpretation skills\n",
- "\n",
- "Draft of the manuscript\n",
- "\n",
- "Janina Soler Wenglein [](https://orcid.org/0000-2222-1111-3333) ([Universität Bielefeld, Medizinische Fakultät OWL](https://www.uni-bielefeld.de/fakultaeten/medizin/)) \n",
- "[Hendrik Friederichs](https://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=251340451) [](https://orcid.org/0000-0001-9671-5235) ([Universität Bielefeld, Medizinische Fakultät OWL](https://www.uni-bielefeld.de/fakultaeten/medizin/)) \n",
- "23. Januar 2024\n",
- "\n",
- "**Background / Hintergrund**: …\n",
- "\n",
- "**Methods / Methoden**: …\n",
- "\n",
- "**Results / Ergebnisse**: …\n",
- "\n",
- "**Conclusio / Schlussfolgerungen**: …\n",
- "\n",
- "<sup>1</sup> Universität Bielefeld, Medizinische Fakultät OWL\n",
- "\n",
- "<sup>✉</sup> Correspondence: [Hendrik Friederichs \\<hendrik.friederichs@uni-bielefeld.de\\>](mailto:hendrik.friederichs@uni-bielefeld.de)\n",
- "\n",
- "> **IN PROGRESS …**\n",
- ">\n",
- "> This manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n",
- ">\n",
- "> <span color=\"grey\">*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal …*</span>\n",
- "\n",
- "> **STRUKTUR DES MANUSKRIPTS**\n",
- ">\n",
- "> <span color=\"grey\">**Relevantes Problem:** Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant. \n",
- "> **Fokussiertes Problem:** Studienlage zu THEMA allgemein und Medical-Education-Kontext; \n",
- "> Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus. \n",
- "> **Gap des Problems:** <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend. \n",
- "> **Lösung?:** <!-- möglicher Fortschritt / mögliche Lösung - Ansprechen von Motiv(en): Anschluss, Leistung, Macht --> Gibt es einen Fortschritt durch bessere Leistungen der neuen Möglichkeiten? \n",
- "> **Forschungsfragen:** Wie ist die absolute Leistung von THEMA im Progress Test Medizin? \n",
- "> Wie ist die relative Leistung im Vergleich zu Medizinstudierenden? \n",
- "> Wie sieht die Leistung bei detaillierter Betrachtung der Domänen und Kompetenzlevel aus? \n",
- "> **Studienpopulation:** Medizinstudierende \n",
- "> **Studiendesign:** Kontrollierte Studie \n",
- "> **Datenerhebung:** 200 Multiple-Choice-Fragen aus dem Progress Test Medizin \n",
- "> **Ergebnisparameter:** Anzahl der richtigen Antworten insgesamt und pro Domäne bzw. Kompetenzlevel \n",
- "> **Statistik:** Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen.</span>\n",
- "\n",
- "## Background\n",
- "\n",
- "### Broad problem\n",
- "\n",
- "Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n",
- "\n",
- "### Theoretical and/or empirical focus of the problem\n",
- "\n",
- "One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n",
- "\n",
- "Processing those visual representations is essential for understanding scientific and statistical data \\[[1](#ref-friel2001making)\\] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n",
- "\n",
- "In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n",
- "\n",
- "### Focused problem statement\n",
- "\n",
- "Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n",
- "\n",
- "When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n",
- "\n",
- "### Statement of study intent\n",
- "\n",
- "We performed a study of medical students to investigate the following questions:\n",
- "\n",
- "1. What is …\n",
- "2. Why are …\n",
- "\n",
- "## Methods\n",
- "\n",
- "### Setting and subjects\n",
- "\n",
- "Our study was conducted at Medical Faculty of Münster …\n",
- "\n",
- "It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students’ clinical experience, they are rotated in various hospital departments during their final year (“clinical/practical†year). …\n",
- "\n",
- "### Study design / Studiendesign\n",
- "\n",
- "The participants were asked to complete the graph literacy scale voluntarily and anonymously.\n",
- "\n",
- "### Ethical approval\n",
- "\n",
- "All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
- "\n",
- "### Data collection\n",
- "\n",
- "Data collection for this study was determined à priori as follows:\n",
- "\n",
- "- Input …\n",
- "\n",
- "``` {webr-r}\n",
- "#| context: setup\n",
- "\n",
- "# Download a dataset\n",
- "download.file(\n",
- " 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n",
- " 'penguins.csv'\n",
- ")\n",
- "\n",
- "# Read the data\n",
- "df_penguins = read.csv(\"penguins.csv\")\n",
- "```\n",
- "\n",
- "### Outcome Measures / Ergebnisparameter\n",
- "\n",
- "…\n",
- "\n",
- "### Statistical methods / Statistische Methoden\n",
- "\n",
- "We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) \\[[2](#ref-hattie2023visible)\\] in outcome level between intervention and control groups (calculated *a priori* with G\\*Power 3.1) \\[[3](#ref-faul2007g)\\]. Statistical analysis, tables and figures were conducted using R \\[[4](#ref-R-base)\\] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages \\[[5](#ref-tidyverse)–[7](#ref-patil2021visualizations)\\]. Descriptive means and standard deviations were calculated for participants’ age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. …\n",
- "\n",
- "``` {webr-r}\n",
- "#| context: interactive\n",
- "\n",
- "# Download a dataset\n",
- "download.file(\n",
- " 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n",
- " 'penguins.csv'\n",
- ")\n",
- "\n",
- "# Read the data\n",
- "penguins = read.csv(\"penguins.csv\")\n",
- "\n",
- "# Scatterplot example: penguin bill length versus bill depth\n",
- "ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) +\n",
- " ggplot2::geom_point(ggplot2::aes(color = species, \n",
- " shape = species),\n",
- " size = 2) +\n",
- " ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\"))\n",
- "```\n",
- "\n",
- "Zeile 7 \n",
- "Download the dataset\n",
- "\n",
- "Zeile 10 \n",
- "Read the data\n",
- "\n",
- "Zeilen 13,15,17 \n",
- "Build a scatterplot\n",
- "\n",
- "## Results / Ergebnisse\n",
- "\n",
- "### Recruitment Process and Demographic Characteristics / Studienteilnahme\n",
- "\n",
- "The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …"
- ],
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- },
- {
- "cell_type": "raw",
- "metadata": {
- "raw_mimetype": "text/html"
- },
- "source": [
- "<!-- Man kann Code-Ergebnisse über einfügen -->"
- ],
- "id": "5696d52b-f232-45ba-b55c-bbd8229fc1f8"
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Primary and secondary Outcomes / Haupt- und Nebenergebnisse\n",
- "\n",
- "<figure>\n",
- "<img src=\"attachment:Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg\" alt=\"Beispielgrafik: ein Bild sagt mehr als tausend Worte …\" />\n",
- "<figcaption aria-hidden=\"true\">Beispielgrafik: ein Bild sagt mehr als tausend Worte …</figcaption>\n",
- "</figure>"
- ],
- "attachments": {
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3LGO7kbfevyZ1r/goB8YPiJ+2o/wi+EvjDwjoXw10TULRZb++urUR6nZRSwrd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\nUgv7VX/YS1z/ANPclfT+pf8ABPvxHpv7Xc37Unwy+Kd14Wj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A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\n+a1/KGvq2gArH1nW7PRLd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- "## Discussion / Diskussion\n",
- "\n",
- "### Summary / Zusammenfassung der Ergebnisse\n",
- "\n",
- "After the evaluation of all datasets, the following findings emerged. The first is that …\n",
- "\n",
- "### Limitation: study population\n",
- "\n",
- "### Limitation: study ndesign\n",
- "\n",
- "### Integration with prior work\n",
- "\n",
- "…\n",
- "\n",
- "Only a few studies provide insights into the graphical and numerical skills among medical students.\n",
- "\n",
- "In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales’ mean scores were high among the medical students in this sample \\[[8](#ref-mas2018graphical)\\].\n",
- "\n",
- "### Implications for practice\n",
- "\n",
- "…\n",
- "\n",
- "### Implications for research\n",
- "\n",
- "…\n",
- "\n",
- "## Conclusions\n",
- "\n",
- "…\n",
- "\n",
- "## References\n",
- "\n",
- "1\\. Friel SN, Curcio FR, Bright GW. Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in mathematics Education. 2001;32:124–58.\n",
- "\n",
- "2\\. Hattie J. Visible learning: The sequel: A synthesis of over 2,100 meta-analyses relating to achievement. Taylor & Francis; 2023.\n",
- "\n",
- "3\\. Faul F, Erdfelder E, Lang A-G, Buchner A. G\\* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods. 2007;39:175–91.\n",
- "\n",
- "4\\. R Core Team. [R: A Language and Environment for Statistical Computing](https://www.R-project.org). Vienna, Austria: R Foundation for Statistical Computing; 2019.\n",
- "\n",
- "5\\. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, u. a. [Welcome to the tidyverse](https://doi.org/10.21105/joss.01686). 2019;4:1686.\n",
- "\n",
- "6\\. Iannone R, Cheng J, Schloerke B, Hughes E, Seo J. [gt: Easily Create Presentation-Ready Display Tables](https://CRAN.R-project.org/package=gt). 2022.\n",
- "\n",
- "7\\. Patil I. Visualizations with statistical details: The’ggstatsplot’approach. Journal of Open Source Software. 2021;6:3167.\n",
- "\n",
- "8\\. Mas G, Tello T, Ortiz P, Petrova D, GarcıÌa-Retamero R. Graphical and numerical skills in pre-and postgraduate medical students from a private university. Gac Med Mex. 2018;154:163–9.\n",
- "\n",
- "## Declarations\n",
- "\n",
- "### Ethics approval and consent to participate\n",
- "\n",
- "Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
- "\n",
- "### Consent for publication\n",
- "\n",
- "Not applicable\n",
- "\n",
- "### Availability of data and materials\n",
- "\n",
- "The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n",
- "\n",
- "### Competing interests / Konkurrierende Interessen\n",
- "\n",
- "The authors declare that they have no competing interests.\n",
- "\n",
- "### Funding / Finanzierung\n",
- "\n",
- "The author(s) received no specific funding for this work.\n",
- "\n",
- "### Authors’ contributions / Beiträge der Autor\\*innen\n",
- "\n",
- "HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.\n",
- "\n",
- "### CRediT authorship contribution statement\n",
- "\n",
- "**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n",
- "\n",
- "### Acknowledgments / Danksagung\n",
- "\n",
- "The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility."
- ],
- "id": "1e1748df-86d6-44cf-bb9f-89bc237b5b1d"
- }
- ],
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diff --git a/public/index.qmd b/public/index.qmd
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@@ -1,217 +0,0 @@
----
-title: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills
-subtitle: Draft of the manuscript
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- - Artificial Intelligence
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- Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.
-key-points:
- - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.
- - Medical education is a multidisciplinary field of medicine, education, and psychology.
-date: last-modified
-citeproc: true
-bibliography: references.bib
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----
-
-## Abstract
-
-**Background / Hintergrund**: ...
-
-**Methods / Methoden**: ...
-
-**Results / Ergebnisse**: ...
-
-**Conclusio / Schlussfolgerungen**: ...
-
-------------------------------------------------------------------------
-
-::: {.callout-caution title="IN PROGRESS ..."}
-This manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.
-
-[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color="grey"}
-:::
-
-::: {.callout-tip title="STRUKTUR DES MANUSKRIPTS" collapse="true"}
-[{{< meta plain-language-summary >}}]{color="grey"}
-:::
-
-{{< include _background.md >}}
-
-## Methods
-
-### Setting and subjects
-
-Our study was conducted at Medical Faculty of Münster ...
-
-It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year ("clinical/practical" year). ...
-
-### Study design / Studiendesign
-
-The participants were asked to complete the graph literacy scale voluntarily and anonymously.
-
-### Ethical approval
-
-All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).
-
-### Data collection
-
-Data collection for this study was determined à priori as follows:
-
-- Input ...
-
-```{webr-r}
-#| context: setup
-
-# Download a dataset
-download.file(
- 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
- 'penguins.csv'
-)
-
-# Read the data
-df_penguins = read.csv("penguins.csv")
-```
-
-### Outcome Measures / Ergebnisparameter
-
-...
-
-### Statistical methods / Statistische Methoden
-
-We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) [@hattie2023visible] in outcome level between intervention and control groups (calculated *a priori* with G\*Power 3.1) [@faul2007g]. Statistical analysis, tables and figures were conducted using R [@R-base] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages [@tidyverse; @gt; @patil2021visualizations]. Descriptive means and standard deviations were calculated for participants' age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. ...
-
-```{webr-r}
-#| context: interactive
-
-# Download a dataset
-download.file(
- 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
- 'penguins.csv'
-) # <1>
-
-# Read the data
-penguins = read.csv("penguins.csv") # <2>
-
-# Scatterplot example: penguin bill length versus bill depth
-ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # <3>
- ggplot2::geom_point(ggplot2::aes(color = species,
- shape = species), # <3>
- size = 2) +
- ggplot2::scale_color_manual(values = c("darkorange","darkorchid","cyan4")) # <3>
-```
-
-1. Download the dataset
-2. Read the data
-3. Build a scatterplot
-
-## Results / Ergebnisse
-
-### Recruitment Process and Demographic Characteristics / Studienteilnahme
-
-The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...
-
-<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
-
-### Primary and secondary Outcomes / Haupt- und Nebenergebnisse
-
-
-
-<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
-
-## Discussion / Diskussion
-
-### Summary / Zusammenfassung der Ergebnisse
-
-After the evaluation of all datasets, the following findings emerged. The first is that ...
-
-### Limitation: study population
-
-### Limitation: study ndesign
-
-### Integration with prior work
-
-...
-
-Only a few studies provide insights into the graphical and numerical skills among medical students.
-
-In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].
-
-### Implications for practice
-
-...
-
-### Implications for research
-
-...
-
-## Conclusions
-
-...
-
-## References {.unnumbered}
-
-::: {#refs}
-:::
-
-## Declarations {.appendix}
-
-### Ethics approval and consent to participate
-
-Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).
-
-### Consent for publication
-
-Not applicable
-
-### Availability of data and materials
-
-The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).
-
-### Competing interests / Konkurrierende Interessen
-
-The authors declare that they have no competing interests.
-
-### Funding / Finanzierung
-
-The author(s) received no specific funding for this work.
-
-### Authors' contributions / Beiträge der Autor\*innen
-
-HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.
-
-### CRediT authorship contribution statement
-
-**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.
-
-### Acknowledgments / Danksagung
-
-The authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.
diff --git a/public/notebooks/EDA-preview.html b/public/notebooks/EDA-preview.html
index 8bf9230aa642fbb7bacca5b64e7dc677d8dcde17..28caa8874f8f6a77e13229d56f865f0b5ac9b15c 100644
--- a/public/notebooks/EDA-preview.html
+++ b/public/notebooks/EDA-preview.html
@@ -1,7 +1,7 @@
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
<meta charset="utf-8">
- <meta name="generator" content="quarto-1.4.547">
+ <meta name="generator" content="quarto-1.5.3">
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
@@ -153,12 +153,10 @@
<script src="../site_libs/quarto-html/tippy.umd.min.js"></script>
<script src="../site_libs/quarto-html/anchor.min.js"></script>
<link href="../site_libs/quarto-html/tippy.css" rel="stylesheet">
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-<link href="../site_libs/quarto-html/quarto-syntax-highlighting-dark.css" rel="prefetch" class="quarto-color-scheme quarto-color-alternate" id="quarto-text-highlighting-styles">
+<link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles">
<script src="../site_libs/bootstrap/bootstrap.min.js"></script>
<link href="../site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
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-<link href="../site_libs/bootstrap/bootstrap-dark.min.css" rel="prefetch" class="quarto-color-scheme quarto-color-alternate" id="quarto-bootstrap" data-mode="dark">
+<link href="../site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
<script src="../site_libs/quarto-contrib/glightbox/glightbox.min.js"></script>
<link href="../site_libs/quarto-contrib/glightbox/glightbox.min.css" rel="stylesheet">
<link href="../site_libs/quarto-contrib/glightbox/lightbox.css" rel="stylesheet">
@@ -173,10 +171,10 @@
<body class="quarto-notebook">
<div id="quarto-embed-header" class="headroom fixed-top bg-primary">
- <a onclick="window.quartoBackToArticle(); return false;" class="btn btn-primary quarto-back-link" href=""><i class="bi bi-caret-left"></i> Zurück zum Artikel</a>
+ <a onclick="window.quartoBackToArticle(); return false;" class="btn btn-primary quarto-back-link" href=""><i class="bi bi-caret-left"></i> Back to Article</a>
<h6><i class="bi bi-journal-code"></i> Data Analysis</h6>
- <a href="../notebooks/EDA.qmd" class="btn btn-primary quarto-download-embed" download="EDA.qmd">Quellcode herunterladen</a>
+ <a href="../notebooks/EDA.qmd" class="btn btn-primary quarto-download-embed" download="EDA.qmd">Download Source</a>
</div>
<header id="title-block-header" class="quarto-title-block default toc-left page-columns page-full">
@@ -221,7 +219,7 @@
</ul>
</nav>
</div>
-<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+<div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
</div>
<main class="content quarto-banner-title-block" id="quarto-document-content">
@@ -256,21 +254,18 @@ dttm (1): DateTime
</div></div>
<p>Create spatial plot:</p>
<div class="cell-container"><div class="cell-decorator"><pre>In [3]:</pre></div><div id="cell-fig-spatial-plot" class="cell">
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-spatial-plot</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: "Locations of earthquakes on La Palma since 2017"</span></span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-alt: "A scatterplot of earthquake locations plotting latitude</span></span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="co">#| against longitude."</span></span>
-<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>la_palma <span class="sc">|></span> </span>
-<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(Longitude, Latitude)) <span class="sc">+</span></span>
-<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> Magnitude, <span class="at">size =</span> <span class="dv">40</span><span class="sc">-</span><span class="st">`</span><span class="at">Depth(km)</span><span class="st">`</span>)) <span class="sc">+</span></span>
-<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_viridis_c</span>(<span class="at">direction =</span> <span class="sc">-</span><span class="dv">1</span>) <span class="sc">+</span> </span>
-<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_size</span>(<span class="at">range =</span> <span class="fu">c</span>(<span class="fl">0.5</span>, <span class="dv">2</span>), <span class="at">guide =</span> <span class="st">"none"</span>) <span class="sc">+</span></span>
-<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| against longitude."</span></span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>la_palma <span class="sc">|></span> </span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(Longitude, Latitude)) <span class="sc">+</span></span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> Magnitude, <span class="at">size =</span> <span class="dv">40</span><span class="sc">-</span><span class="st">`</span><span class="at">Depth(km)</span><span class="st">`</span>)) <span class="sc">+</span></span>
+<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_viridis_c</span>(<span class="at">direction =</span> <span class="sc">-</span><span class="dv">1</span>) <span class="sc">+</span> </span>
+<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_size</span>(<span class="at">range =</span> <span class="fu">c</span>(<span class="fl">0.5</span>, <span class="dv">2</span>), <span class="at">guide =</span> <span class="st">"none"</span>) <span class="sc">+</span></span>
+<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_bw</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div id="fig-spatial-plot" class="quarto-figure quarto-figure-center quarto-float anchored" alt="A scatterplot of earthquake locations plotting latitude against longitude.">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-spatial-plot-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca" class="">
-<a href="EDA_files/figure-html/fig-spatial-plot-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" data-glightbox="description: .lightbox-desc-1"><img src="EDA_files/figure-html/fig-spatial-plot-1.png" class="img-fluid figure-img" alt="A scatterplot of earthquake locations plotting latitude against longitude." width="672"></a>
+<a href="EDA_files/figure-html/fig-spatial-plot-1.png" class="lightbox" data-glightbox="description: .lightbox-desc-1" data-gallery="quarto-lightbox-gallery-1"><img src="EDA_files/figure-html/fig-spatial-plot-1.png" class="img-fluid figure-img" alt="A scatterplot of earthquake locations plotting latitude against longitude." width="672"></a>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-spatial-plot-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Locations of earthquakes on La Palma since 2017
@@ -280,16 +275,13 @@ Locations of earthquakes on La Palma since 2017
</div>
</div></div>
<div class="cell-container"><div class="cell-decorator"><pre>In [4]:</pre></div><div id="cell-fig-map" class="cell">
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-map</span></span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: "Locations of earthquakes on La Palma since 2017"</span></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-alt: "A scatterplot of earthquake locations plotting latitude</span></span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co">#| against longitude."</span></span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">include_graphics</span>(<span class="st">"images/la-palma-map.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| against longitude."</span></span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">include_graphics</span>(<span class="st">"images/la-palma-map.png"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div id="fig-map" class="quarto-figure quarto-figure-center quarto-float anchored" alt="A scatterplot of earthquake locations plotting latitude against longitude.">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-map-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca" class="">
-<a href="images/la-palma-map.png" class="lightbox" data-gallery="quarto-lightbox-gallery-2" data-glightbox="description: .lightbox-desc-2"><img src="images/la-palma-map.png" class="img-fluid figure-img" alt="A scatterplot of earthquake locations plotting latitude against longitude."></a>
+<a href="images/la-palma-map.png" class="lightbox" data-glightbox="description: .lightbox-desc-2" data-gallery="quarto-lightbox-gallery-2"><img src="images/la-palma-map.png" class="img-fluid figure-img" alt="A scatterplot of earthquake locations plotting latitude against longitude."></a>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-map-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Locations of earthquakes on La Palma since 2017
@@ -301,9 +293,8 @@ Locations of earthquakes on La Palma since 2017
<section id="second-text" class="level2">
<h2 class="anchored" data-anchor-id="second-text">Second Text {#}</h2>
<div class="cell-container"><div class="cell-decorator"><pre>In [5]:</pre></div><div class="cell">
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: sec-text</span></span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="fu">paste0</span>(<span class="st">"Hier ein weiterer, ergänzender Text"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="fu">paste0</span>(<span class="st">"Hier ein weiterer, ergänzender Text"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Hier ein weiterer, ergänzender Text</code></pre>
</div>
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diff --git a/public/notebooks/EDA.out.ipynb b/public/notebooks/EDA.out.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..586d05b07ed3cb8554e3410c8a52d65f7a3236b8
--- /dev/null
+++ b/public/notebooks/EDA.out.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# (Explorative) Data Analysis\n",
+ "\n",
+ "Hendrik Friederichs"
+ ],
+ "id": "068b79da-43b8-4337-8c01-0e1a9716696a"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n",
+ "✔ dplyr 1.1.4 ✔ readr 2.1.5\n",
+ "✔ forcats 1.0.0 ✔ stringr 1.5.1\n",
+ "✔ ggplot2 3.4.4 ✔ tibble 3.2.1\n",
+ "✔ lubridate 1.9.3 ✔ tidyr 1.3.0\n",
+ "✔ purrr 1.0.2 \n",
+ "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n",
+ "✖ dplyr::filter() masks stats::filter()\n",
+ "✖ dplyr::lag() masks stats::lag()\n",
+ "ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors"
+ ]
+ }
+ ],
+ "source": [
+ "library(tidyverse)\n"
+ ],
+ "id": "1de3abe9-eeb5-4ade-9b4b-fac712377f74"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Read a clean version of data:"
+ ],
+ "id": "19b9bd6a-262d-4eb6-9e6f-a2adf2a4395e"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Rows: 11347 Columns: 5\n",
+ "── Column specification ────────────────────────────────────────────────────────\n",
+ "Delimiter: \",\"\n",
+ "dbl (4): Longitude, Latitude, Depth(km), Magnitude\n",
+ "dttm (1): DateTime\n",
+ "\n",
+ "ℹ Use `spec()` to retrieve the full column specification for this data.\n",
+ "ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message."
+ ]
+ }
+ ],
+ "source": [
+ "la_palma <- read_csv(\"la-palma.csv\")\n"
+ ],
+ "id": "b13f1010-3382-4735-bd5e-41479898b822"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create spatial plot:"
+ ],
+ "id": "8c8de608-4cba-40ea-8547-525c8b629d83"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "metadata": {},
+ "data": {
+ "image/png": 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qK+fT0aNHu3YjISEBs2bNwrfffosnnngCV199NSZOnIgNGzbg1VdfdaabM2cOYmNjj+RR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\nAFBnpEFx0zo/5WMKBKWA9H7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AlXkxNQXCQmBr9X5sqy2X3detNaUoaqzGsCRll4rKroAJ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hOFOJYgxwgdE+8bXBX7nKnbeelzsZJinvpTOCnUlKTJ/nQZkDXZOrCj6d8obvucIJseob6ZmHp6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1X7JPWifLXJEaiHqSg+UIp9zs0L9l9LV5Gcbf205KYWyNmFF0l+yO+tsU52W6ZsW1/jBz/1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\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GH0b8sdxchf4sF2ri7066/u46VKcAUK1Lc1lOgH/5iBoLZOgF85Mm45XYGOHmQDbhfCQj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KCvTJNboX5gkiQTmN29bCtXrhSdyULj7733XlxzzTVYtmyZqBwfOPMInDEClJnOwsCuvPJK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\ngmd2Tb2ZkLDMjCUoGO4+HjP9l7BwBzzeo+HK8MBQfDv3QTz3+wa1n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fqki9YsM/MzA5ch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\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NWAxQIzw6HBZN2KEEyADaobhpMiJABIgAEejKBHYwr88zeae0qljbWIN1qd+CZ76mQgRa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\nTkSuQmCC\n"
+ }
+ }
+ ],
+ "source": [
+ "#| against longitude.\"\n",
+ "la_palma |> \n",
+ " ggplot(aes(Longitude, Latitude)) +\n",
+ " geom_point(aes(color = Magnitude, size = 40-`Depth(km)`)) +\n",
+ " scale_color_viridis_c(direction = -1) + \n",
+ " scale_size(range = c(0.5, 2), guide = \"none\") +\n",
+ " theme_bw()\n"
+ ],
+ "id": "cell-fig-spatial-plot"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "metadata": {},
+ "data": {
+ "image/png": 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AcyYX81/XTyfwv75cMBDg3YureffiarY3drN8Xwcr93ewsraDrQ1ddA/EALh99XZOmVjN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\ny/fv6ft81qxJTiAAAPCUFQrJOy8/K8vm9N5TrNnbkvP+47588b49whlib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+ }
+ }
+ ],
+ "source": [
+ "#| against longitude.\"\n",
+ "knitr::include_graphics(\"images/la-palma-map.png\")\n"
+ ],
+ "id": "cell-fig-map"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Second Text {#}"
+ ],
+ "id": "c464c02e-2929-4563-aa98-fa4c457ca976"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Hier ein weiterer, ergänzender Text"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "cat(paste0(\"Hier ein weiterer, ergänzender Text\"))\n"
+ ],
+ "id": "0527545d-2561-42f3-a375-065872cc0458"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Third Text\n",
+ "\n",
+ "Dieser Text zur Abgrenzung."
+ ],
+ "id": "bc86343e-e2fb-42d1-b3af-6ceff72dffcd"
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/public/notebooks/EDA.qmd b/public/notebooks/EDA.qmd
new file mode 100644
index 0000000000000000000000000000000000000000..ca13fc614483f865bdc47bc4caf51bddc823a33e
--- /dev/null
+++ b/public/notebooks/EDA.qmd
@@ -0,0 +1,51 @@
+---
+title: (Explorative) Data Analysis
+author: Hendrik Friederichs
+---
+
+
+```{r setup, include = FALSE}
+library(tidyverse)
+```
+
+Read a clean version of data:
+
+```{r}
+la_palma <- read_csv("la-palma.csv")
+```
+
+Create spatial plot:
+
+```{r}
+#| label: fig-spatial-plot
+#| fig-cap: "Locations of earthquakes on La Palma since 2017"
+#| fig-alt: "A scatterplot of earthquake locations plotting latitude
+#| against longitude."
+la_palma |>
+ ggplot(aes(Longitude, Latitude)) +
+ geom_point(aes(color = Magnitude, size = 40-`Depth(km)`)) +
+ scale_color_viridis_c(direction = -1) +
+ scale_size(range = c(0.5, 2), guide = "none") +
+ theme_bw()
+```
+
+```{r}
+#| label: fig-map
+#| fig-cap: "Locations of earthquakes on La Palma since 2017"
+#| fig-alt: "A scatterplot of earthquake locations plotting latitude
+#| against longitude."
+knitr::include_graphics("images/la-palma-map.png")
+```
+
+## Second Text {#}
+
+```{r}
+#| label: sec-text
+
+cat(paste0("Hier ein weiterer, ergänzender Text"))
+```
+
+
+## Third Text
+
+Dieser Text zur Abgrenzung.
diff --git a/public/notebooks/images/la-palma-map.png b/public/notebooks/images/la-palma-map.png
new file mode 100644
index 0000000000000000000000000000000000000000..8c8045e7ab9cfa78c4795ed76b15e7962dad6b28
Binary files /dev/null and b/public/notebooks/images/la-palma-map.png differ
diff --git a/text/_methods.md b/text/_methods.md
index 902afd1ef77ae7263b70c95df68c7dcb1ff7ef2f..f9c4a68feba6585eb968558b2e10e83b2f5b37f3 100644
--- a/text/_methods.md
+++ b/text/_methods.md
@@ -57,6 +57,4 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
size = 2) +
ggplot2::scale_color_manual(values = c("darkorange","darkorchid","cyan4"))
```
-1.
-2.
-3.
\ No newline at end of file
+