From d1385cf13f27db67a3a0e2de513a4ffc46930930 Mon Sep 17 00:00:00 2001
From: Hendrik Friederichs <hendrik.friederichs@uni-bielefeld.de>
Date: Mon, 29 Apr 2024 12:19:15 +0200
Subject: [PATCH] Update 29-04-2024
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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 - text/_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: text/references.bib\nzotero: \"graph-literacy-progress\"\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: en\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n\n**Background**: 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, including understanding graphs properly. Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. When providing information to patients graph literacy is a form of semiotic activity that is a crucial component of overall literacy for (future) medical doctors. \n\n**Methods**: A cross-sectional study was performed with medical students in their academic years 1, 2, 3, 4 and 5 at the medical school of the University of Muenster, Germany. We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information.\n\n**Results**: {{< lipsum 1 >}}\n\n**Conclusio**: The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent.\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=\"gray\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"gray\"}\n:::\n\n## Background\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 [@cooper2002graphical]. 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 [@melnik2023my].\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy [@roth2002reading]. It can have an impact on risk comprehension [@okan2019using], suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients [@durand2020graph] or the ability of doctors [@caverly2015doctors] 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## Methods\n\n### Setting and subjects\n\nOur cross-sectional study was performed with medical students in their academic years 1, 2, 3, 4 and 5 at the medical school of the University of Muenster, 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\nGraph literacy can be measured with the so-called 'graph literacy scale' introduced by Galesic et al. in 2011 [@galesic2011]. The students were asked to participate in the study and answer the questions of the \"Graph Literacy Scale\" (Galesic and Garcia-Retamero, 2011) with pen and paper. The validated questionnaire was handed out during April 2017 to June 2017. Completion of the questionnaire was voluntary, anonymously and performed in the context of the progress test. A mandatory examination, taking place annually at the medical faculty. No calculators were allowed. We did not ask participants about their social, biographical or educational background, but a post-secondary school diploma (abitur) is necessary for entering medical school and a statistical curriculum is part of the school education in the German upper school.\n\n### Ethical approval\n\nApproval was obtained from the local Ethics Committee University Muenster, Germany.\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\nWe did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information. The students had to answer 9 open and 4 multiple choice questions. Each of the questions represents one of the 3 levels of graph comprehension: reading the data, reading between the data, reading beyond the data (Galesic and Garcia-Retamero, 2011). Except for question 7 there is only one correct answer to every question. Question 7 does accept a range (\\>23 to \\>25) of correct answers. Participants received one point for every correct solution, meaning they could achieve 0 to 13 points in total. Only the correct answer was taken into account and only the defined exact numbers were considered correct in open questions. There was no error margin, questionnaires including blank responses were not considered for our analysis.\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\")\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## 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## 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@Nayak2016 highlights the importance of graph literacy in patient-centered communication tools and its correlation with dashboard comprehension.\n\n@Gaissmaier2012 emphasizes the significance of graph literacy in understanding health-related statistical information and its relationship with comprehension and recall.\n\n@Durand2020 examines the relationship between graph literacy, numeracy, health literacy, and sociodemographic characteristics in a Medicaid-eligible population.\n\n@Tolbert2018 discusses the impact of different graphical representations on the accurate interpretation and clarity of patient-reported outcome scores in cancer patients/survivors.\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\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 - text/_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: text/references.bib\nzotero: \"graph-literacy-progress\"\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: en\neditor:\n markdown:\n canonical: true\n---\n\n\n\n\n\n\n## Abstract\n\n\n\n**Background**: 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, including understanding graphs properly. Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. When providing information to patients graph literacy is a form of semiotic activity that is a crucial component of overall literacy for (future) medical doctors. \n\n**Methods**: A cross-sectional study was performed with medical students in their academic years 1, 2, 3, 4 and 5 at the medical school of the University of Muenster, Germany. We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information.\n\n**Results**: The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although the students had an excellent overall performance, there was one question (question 11) achieving poor results with an better performance in higher academic years but still a ceiling effect on a low level with a maximum of 57,6% correct answers. \n\n**Conclusion**: Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent. This leads to implications for training of medical students and need of further research in this domain.\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=\"gray\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"gray\"}\n:::\n\n\n## Background\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, interpreting of epidemiological data and communicating 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, interpreting and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and is known as semiotic activity. When informations are provided graphically the underlying assumption, this could reduce the cognitive work load and would be easier to understand, seems obvious. Thus, the ability to understand graphs should not be considered isolated 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 [@cooper2002graphical]. 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 [@melnik2023my].\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy [@roth2002reading]. It can have an impact on risk comprehension [@okan2019using], suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients [@durand2020graph] or the ability of doctors [@caverly2015doctors] to interpret graphical representations. Data regarding medical students ability of graph literacy and investigations on how to increase these are lacking. \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. How high is the graph literacy among medical students?\n2. Does the graph literacy of medical students change during their respective academic progress (measured in years)?\n3. Are there any sub-areas of medical students' graph literacy that require special attention?\n\n## Methods\n\n### Setting and subjects\n\nOur cross-sectional study was performed with medical students in their academic years 1, 2, 3, 4 and 5 at the medical school of the University of Muenster, 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\nGraph literacy can be measured with the so-called 'graph literacy scale' introduced by Galesic et al. in 2011 [@galesic2011]. The students were asked to participate in the study and answer the questions of the \"Graph Literacy Scale\" (Galesic and Garcia-Retamero, 2011) with pen and paper. The validated questionnaire was handed out during April 2017 to June 2017. Completion of the questionnaire was voluntary, anonymously and performed in the context of the progress test. A mandatory examination, taking place annually at the medical faculty. No calculators were allowed. We did not ask participants about their social, biographical or educational background, but a post-secondary school diploma (abitur) is necessary for entering medical school and a statistical curriculum is part of the school education in the German upper school.\n\n### Ethical approval\n\nApproval was obtained from the local Ethics Committee University Muenster, Germany.\n\n### Data collection\n\nData collection for this study was determined à priori as follows: students of each study year of the medical University Muenster are asked to voluntarily participate and answer questions of the \"Graph Literacy Scale\". The study takes place during the annual progress test, a obligatory test students of all study years have to take. \n\n### Outcome Measures\n\nWe did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information. The students had to answer 9 open and 4 multiple choice questions. Each of the questions represents one of the 3 levels of graph comprehension: reading the data, reading between the data, reading beyond the data (Galesic and Garcia-Retamero, 2011). Except for question 7 there is only one correct answer to every question. Question 7 does accept a range (\\>23 to \\>25) of correct answers. Participants received one point for every correct solution, meaning they could achieve 0 to 13 points in total. Only the correct answer was taken into account and only the defined exact numbers were considered correct in open questions. There was no error margin, questionnaires including blank responses were not considered for our analysis. The average score (standard\ndeviation) in this test format according to Galesic and Garcia-Retamero (2011) is 9.4 (standard deviation 2.6) for Germany. \n\n### Statistical methods\n\nAll data were entered and alyzed using the statistial software R Studio 2023.12.1. Descriptive means and standard deviations were calculated for participants' age and mean test scores were computed for all participants' and for each study year seperately. \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\")\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## Results\n\n### Recruitment Process and Demographic Characteristics\n\nWe obtained 449 complete questionnaires, which could be included in our analysis. Out of a total of 500 questionnaires 31 surveys were actively canceled by the respondents and therefore not considered. Out of those 469 left over, 20 were lacking answers regarding individual responses and therefor haven’t been included. 2 questionnaires were lacking information about the sex of the respondents, 1 was lacking information regarding age, those 3 weren’t excluded. Out of our 449 included questionnaires 126 students were in their first academic year, 128 students in their second academic year, 54 students in their third year, 59 students in their fourth year and 82 students in their fifth year. 151 students stated their sex was male, 296 stated their sex was female, 2 didn't give informations regarding sex.\n\n### Primary and secondary Outcomes\n\nAfter the evaluation of all datasets, three major findings were obtained. The first finding is that participants achieved an average score of 11.42 (s = 1.42) points, which is significantly higher than the average score of Galesic (2011) with 9.4 (s = 2.6) points. The second major finding is, although there is a slight increase in the scores between the first (11.33 points [s = 1.40]) to the fifth academic year (11.65 [s = 1.18]), no significant difference could be detected (p = xx). Finally, although the students had an excellent overall performance, there was one question (question 11, testing abilities to read beyond the data) achieving poor results with an increase of correct answers in the older cohorts, but still a ceiling effect on a low level with a maximum of 57,6% correct answers. \n\n## Discussion\n\nThe first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent. Fluctuation range in the area of reading beyond the data is extremely high between individual questions. A finding also seen in the validation study (Galesic, 2011). Although our medical students perform better than the reference group, the overall result for question 11 is poor with 38.1%-57.3% (mean 48,3%) correct answers. Although there is an improvement in the ability to answer the question across the different cohorts with each year of study, there is a certain ceiling effect. Question 11 requires the comparison between 2 line charts. The participants are asked to decide based on those line charts which treatment contributes to a larger decrease of sick patients. The y-axes in those charts state \"% sick patients\" and does not give informations regarding the scale. The correct answer would be \"can't say\" based on the charts. @Garcia2011\n\nScoring high in other aspects of reading beyond the data, a possible explanation for poor performance of our medical students could be a base rate neglect manifesting with poor results when answering question 11. Whenever the correct answer is “can’t say†also a lacking tolerance of ambiguity could be an explanation for a poor performance. Considering the role of physicians giving answers and guiding therapy, admitting one can’t give an answer, might be especially complex. Low aversion to ambiguity is a known risk factor for suboptimal decision-making in clinical practice, as for example is shown by @Saposnik2011 for treatment in patients with Spinal Muscular Atrophy (SMA). If the assumption that a lack of ambiguity tolerance contributes to the response behavior is correct, this has far-reaching consequences for the need for training of the low graph literacy shown in our medical students cohort.\n\nThere are some limitations to our study, that need to be acknowledged. First, we performed a cross-sectional study, that was carried out on a specific point in time and in the setting of an mandatory test, every medical student has to take. This might have caused students to care less for correct answers or having a lower attention span because they already had to spend severe effort to perform well in their test. We can't say, if participants might have performed much better at a different time or at a later stage of their training, for example as residents in clinical practice. There might be an training effect for graph literacy, that only evoles over time needing to read and explain data regularly to patients or colleagues. Further we didn't ask participants to give specific informations regarding their statistical training in school or during their medical studies. We also can't say why the performance regarding graph literacy was especially inconsistent in \"reading beyond the data\". This question does need further research attention.\nNonetheless, a cross-sectional study design remains an efficient way to evaluate the prevalence of certain skills in a large sample of students. A true statement about how the graph literacy changes can not be given, because we did a single time measurement. But we can say, that in comparisson those students of higher academic experience do perform better when measuring their graph literacy.\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]. This is consistent with our research findings, which also revealed high overall graph literacy skills for medical students. Due to the very good results of the students at the level of data reading skills, the authors of the study recommend a reduction of the graph literacy measurement for medical students to the aspects of reading between and beyond the data. Assuming this could lead to a more objective measurement of graphical skills in trained persons. However, since only a precise record of all aspects of the abilities and weaknesses of medical students provides a comprehensive picture, this abbreviation does not appear to be adequate for research purposes.\n\nLiterature regarding patients' and nurses' graphy literacy does provide further insight with respect to the different levels of graph literacy. For example Durand et al @Durand2020 examine the relationship between graph literacy, numeracy, health literacy, and sociodemographic characteristics in a Medicai-eligible population. They did not find a statistically significant association betwwen Graph literacy and higher education. But they found a positive relationship between graph literacy and numeracy, while numeracy was associated to education. @Gaissmaier2012 emphasizes the significance of graph literacy in understanding health-related statistical information and ability for informed medical decision making. They found a high graph literacy to be associated with a better comprehension and recall for graphics instead of numbers. This makes graph literacy tremendous important in patient-centered communication tools, as shown by @Nayak2016. @Dowding2018 do support this statement for visual displays for clinical dashboards used by nurses. \n\n@Gaissmeier2023 people fail to identify what they comprehend better but base their choice between representations on how attractive the representation is.\n\n@Tolbert2018 discusses the impact of different graphical representations on the accurate interpretation and clarity of patient-reported outcome scores in cancer patients/survivors.\n\n### Implications for practice\n\nDespite some improvement across different cohorts with each year of study, there seems to be a ceiling effect in abilities for reading beyond the data. This suggests that even with progression through medical education, there are limitations in the learning ability for this particular item. A possible base rate neglect and a lacking tolerance of ambiguity as explanations for poor performance, particularly when the correct answer is \"can't say,\" adds a psychological dimension to the interpretation of our results. The reluctance to admit uncertainty, especially in a medical context where physicians are expected to provide answers and guide therapy, could be contributing to this specific challenge.\nFor future training of medical students, addressing these challenges could involve interventions to improve tolerance for ambiguity and emphasize the importance of acknowledging uncertainty when necessary. Training programs might incorporate exercises that specifically target those skills. Additionally, providing awareness and education about base rate neglect could enhance decision-making in situations where uncertainty is inherent.\nFurther it can be deduced that special attention in graph literacy of medical students should also be paid to the ability to communicate visually presented results. This takes into account the doctor as a mediator between specialist knowledge and patient interests, as well as the possibly lower graph literacy skills of patients.\n\n### Implications for research\n\nOur findings suggest that while medical students generally exhibit above-average results in graph literacy, particularly in reading and interpreting data, there are challenges in their ability to go beyond the presented data. This fluctuations in performance does indicate a need for future research. Exploring the psychological factors influencing graph literacy in medical students, such as tolerance of ambiguity, could be valuable to gain a better understanding. Investigating interventions that could improve performance in reading beyond the data, especially in situations involving uncertainty, could contribute to the development of more effective educational strategies.\n\n### Conclusions\n\nIn summary, our findings suggest a nuanced understanding of the strengths and challenges in the graph literacy of medical students, with implications for both their training and need of further research in this domain.\n\n### Acknowledgements\nWe thank Mirta Galesic and Rocio Garcia-Retamero for providing their Graph Literacy scale (pen and paper format). This study had no external funding. \n\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n\n### Ethics approval and consent to participate\n\nApproval was obtained from the local Ethics Committee University Muenster, Germany. A validated questionnaire for \"Graph Literacy\" was handed out to medical students during April 2017 to June 2017. Completion of the questionnaire was voluntary.\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\nWe thank Mirta Galesic and Rocio Garcia-Retamero for providing their Graph Literacy scale (pen and paper format). \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/_freeze/site_libs/quarto-html/quarto.js b/_freeze/site_libs/quarto-html/quarto.js
index 3ebd49c..11803b0 100644
--- a/_freeze/site_libs/quarto-html/quarto.js
+++ b/_freeze/site_libs/quarto-html/quarto.js
@@ -94,7 +94,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
if (link.href.indexOf("#") !== -1) {
const anchor = link.href.split("#")[1];
const heading = window.document.querySelector(
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+ `[data-anchor-id="${anchor}"]`
);
if (heading) {
// Add the class
diff --git a/site_libs/bootstrap/bootstrap-dark.min.css b/index_files/libs/bootstrap/bootstrap-dark.min.css
similarity index 97%
rename from site_libs/bootstrap/bootstrap-dark.min.css
rename to index_files/libs/bootstrap/bootstrap-dark.min.css
index 81e9a53..53e8e1a 100644
--- a/site_libs/bootstrap/bootstrap-dark.min.css
+++ b/index_files/libs/bootstrap/bootstrap-dark.min.css
@@ -9,4 +9,4 @@
* we also add `bright-[color]-` synonyms for the `-[color]-intense` classes since
* that seems to be what ansi_up emits
*
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diff --git a/site_libs/bootstrap/bootstrap-icons.css b/index_files/libs/bootstrap/bootstrap-icons.css
similarity index 100%
rename from site_libs/bootstrap/bootstrap-icons.css
rename to index_files/libs/bootstrap/bootstrap-icons.css
diff --git a/site_libs/bootstrap/bootstrap-icons.woff b/index_files/libs/bootstrap/bootstrap-icons.woff
similarity index 100%
rename from site_libs/bootstrap/bootstrap-icons.woff
rename to index_files/libs/bootstrap/bootstrap-icons.woff
diff --git a/site_libs/bootstrap/bootstrap.min.css b/index_files/libs/bootstrap/bootstrap.min.css
similarity index 97%
rename from site_libs/bootstrap/bootstrap.min.css
rename to index_files/libs/bootstrap/bootstrap.min.css
index dedd877..d7d8e11 100644
--- a/site_libs/bootstrap/bootstrap.min.css
+++ b/index_files/libs/bootstrap/bootstrap.min.css
@@ -9,4 +9,4 @@
* we also add `bright-[color]-` synonyms for the `-[color]-intense` classes since
* that seems to be what ansi_up emits
*
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similarity index 100%
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similarity index 100%
rename from site_libs/quarto-html/anchor.min.js
rename to index_files/libs/quarto-html/anchor.min.js
diff --git a/site_libs/quarto-html/popper.min.js b/index_files/libs/quarto-html/popper.min.js
similarity index 100%
rename from site_libs/quarto-html/popper.min.js
rename to index_files/libs/quarto-html/popper.min.js
diff --git a/site_libs/quarto-html/quarto-syntax-highlighting-dark.css b/index_files/libs/quarto-html/quarto-syntax-highlighting-dark.css
similarity index 100%
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similarity index 98%
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index b30ce57..d9fd98f 100644
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diff --git a/public/index.html b/public/index.html
index ece30d9..8164a7c 100644
--- a/public/index.html
+++ b/public/index.html
@@ -2,7 +2,7 @@
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
<meta charset="utf-8">
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+<meta name="generator" content="quarto-1.5.30">
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<div class="block-title">Abstract</div>
<p><strong>Background</strong>: 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, including understanding graphs properly. Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. When providing information to patients graph literacy is a form of semiotic activity that is a crucial component of overall literacy for (future) medical doctors.</p>
<p><strong>Methods</strong>: A cross-sectional study was performed with medical students in their academic years 1, 2, 3, 4 and 5 at the medical school of the University of Muenster, Germany. We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information.</p>
- <p><strong>Results</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>Conclusio</strong>: The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent.</p>
+ <p><strong>Results</strong>: The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although the students had an excellent overall performance, there was one question (question 11) achieving poor results with an better performance in higher academic years but still a ceiling effect on a low level with a maximum of 57,6% correct answers.</p>
+ <p><strong>Conclusion</strong>: Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent. This leads to implications for training of medical students and need of further research in this domain.</p>
</div>
</div>
@@ -345,13 +342,11 @@ div.csl-indent {
</ul></li>
<li><a href="#discussion" id="toc-discussion" class="nav-link" data-scroll-target="#discussion">Discussion</a>
<ul class="collapse">
- <li><a href="#summary" id="toc-summary" class="nav-link" data-scroll-target="#summary">Summary</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-design" id="toc-limitation-study-design" class="nav-link" data-scroll-target="#limitation-study-design">Limitation: study design</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>
<li><a href="#conclusions" id="toc-conclusions" class="nav-link" data-scroll-target="#conclusions">Conclusions</a></li>
+ <li><a href="#acknowledgements" id="toc-acknowledgements" class="nav-link" data-scroll-target="#acknowledgements">Acknowledgements</a></li>
</ul></li>
<li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
@@ -406,15 +401,15 @@ STRUKTUR DES MANUSKRIPTS
<div id="callout-2" class="callout-2-contents callout-collapse collapse">
<div class="callout-body-container callout-body">
<p><span style="color: gray;"><strong>Relevantes Problem:</strong> 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.<br>
-<strong>Fokussiertes Problem:</strong> 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.<br>
-<strong>Gap des Problems:</strong> <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Graph Literacy beeinflusst das Risikoverständnis und Entscheidungsverhalten, wobei Studien hauptsächlich Patienten und Ärzte im Umgang mit grafischen Darstellungen betrachten.<br>
+<strong>Fokussiertes Problem:</strong> 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. Die Annahme grafische Darstellungen würden grundsätzlich zum vereinfachten Verständnis von Informationen beitragen, ist irreführend. Dies ist besonders relevant in Bereichen wie der medizinischen Forschung, wo Grafiken zur Vermittlung komplexer Informationen genutzt werden. Fehlerhafte rezipierte Darstellungen können ebenso wie Fehldarstellungen die Informationsaufnahme beeinflussen.<br>
+<strong>Gap des Problems:</strong> <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Graph Literacy beeinflusst das Risikoverständnis und Entscheidungsverhalten, wobei Studien hauptsächlich Patienten und Ärzte im Umgang mit grafischen Darstellungen betrachten. Zur Graph Literacy bei Medizinstudierenden sind bislang wenig Forschungsergebnisse publiziert, so dass auch ein tieferes Verständnis davon, wie die Graph Literacy im Medizinstudium gestärkt werden kann, fehlt.<br>
<strong>Lösung?:</strong> <!-- möglicher Fortschritt / mögliche Lösung - Ansprechen von Motiv(en): Anschluss, Leistung, Macht --> Für Ärzte, die Menschen mit geringerer Gesundheitskompetenz betreuen, ist es wichtig, selbst eine hohe Kompetenz im Verständnis visueller Darstellungen zu haben. Diese Fertigkeit sollte schon im Medizinstudium erworben werden.<br>
<strong>Forschungsfragen:</strong> Daher wurde eine Studie mit Medizinstudierenden durchgeführt, um ihre Fähigkeit zur Interpretation visuell dargestellter medizinischer Informationen zu untersuchen.<br>
<strong>Studienpopulation:</strong> Medizinstudierende<br>
<strong>Studiendesign:</strong> Beobachtungsstudie<br>
<strong>Datenerhebung:</strong> Graph Literacy Scale<br>
<strong>Ergebnisparameter:</strong> Anzahl der richtigen Antworten insgesamt.<br>
-<strong>Statistik:</strong> Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen im Studienverlauf. Evtl. noch weitere Vergleichsberechnungen.</span></p>
+<strong>Statistik:</strong> Bestimmung der Prozentwerte für die absolute und z-Scores und Perzentilen für die relative Bewertung der Leistungen im Vergleich der Kohorten unterschiedlicher Studienjahre.</span></p>
</div>
</div>
</div>
@@ -422,11 +417,11 @@ STRUKTUR DES MANUSKRIPTS
<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>
+<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, interpreting of epidemiological data and communicating in medical teams.</p>
</section>
<section id="theoretical-andor-empirical-focus-of-the-problem" class="level3 page-columns page-full">
<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>One important aspect of health literacy is graph literacy, meaning the reading, interpreting and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and is known as semiotic activity. When informations are provided graphically the underlying assumption, this could reduce the cognitive work load and would be easier to understand, seems obvious. Thus, the ability to understand graphs should not be considered isolated 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 <span class="citation" data-cites="cooper2002graphical">[<a href="#ref-cooper2002graphical" role="doc-biblioref">2</a>]</span>. 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 <span class="citation" data-cites="melnik2023my">[<a href="#ref-melnik2023my" role="doc-biblioref">3</a>]</span>.</p>
<div class="no-row-height column-margin column-container"><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.
@@ -442,7 +437,7 @@ STRUKTUR DES MANUSKRIPTS
6. Durand M-A, Yen RW, O’Malley J, Elwyn G, Mancini J. Graph literacy matters: Examining the association between graph literacy, health literacy, and numeracy in a medicaid eligible population. PloS one. 2020;15:e0241844.
</div><div id="ref-caverly2015doctors" class="csl-entry" role="listitem">
7. Caverly TJ, Prochazka AV, Combs BP, Lucas BP, Mueller SR, Kutner JS, et al. Doctors and numbers: An assessment of the critical risk interpretation test. Medical Decision Making. 2015;35:512–24.
-</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 <span class="citation" data-cites="roth2002reading">[<a href="#ref-roth2002reading" role="doc-biblioref">4</a>]</span>. It can have an impact on risk comprehension <span class="citation" data-cites="okan2019using">[<a href="#ref-okan2019using" role="doc-biblioref">5</a>]</span>, suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients <span class="citation" data-cites="durand2020graph">[<a href="#ref-durand2020graph" role="doc-biblioref">6</a>]</span> or the ability of doctors <span class="citation" data-cites="caverly2015doctors">[<a href="#ref-caverly2015doctors" role="doc-biblioref">7</a>]</span> to interpret graphical representations.</p>
+</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 <span class="citation" data-cites="roth2002reading">[<a href="#ref-roth2002reading" role="doc-biblioref">4</a>]</span>. It can have an impact on risk comprehension <span class="citation" data-cites="okan2019using">[<a href="#ref-okan2019using" role="doc-biblioref">5</a>]</span>, suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients <span class="citation" data-cites="durand2020graph">[<a href="#ref-durand2020graph" role="doc-biblioref">6</a>]</span> or the ability of doctors <span class="citation" data-cites="caverly2015doctors">[<a href="#ref-caverly2015doctors" role="doc-biblioref">7</a>]</span> to interpret graphical representations. Data regarding medical students ability of graph literacy and investigations on how to increase these are lacking.</p>
</section>
<section id="focused-problem-statement" class="level3">
<h3 class="anchored" data-anchor-id="focused-problem-statement">Focused problem statement</h3>
@@ -453,8 +448,9 @@ STRUKTUR DES MANUSKRIPTS
<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>
+<li>How high is the graph literacy among medical students?</li>
+<li>Does the graph literacy of medical students change during their respective academic progress (measured in years)?</li>
+<li>Are there any sub-areas of medical students’ graph literacy that require special attention?</li>
</ol>
</section>
</section>
@@ -476,42 +472,26 @@ STRUKTUR DES MANUSKRIPTS
</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>
-<p>…</p>
-<script type="module">
-// Initialization WebR
-await globalThis.webR.init();
-
-// Run R code without focusing on storing data.
-await globalThis.webR.evalRVoid(`
-# 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")
-`)
-</script>
+<p>Data collection for this study was determined à priori as follows: students of each study year of the medical University Muenster are asked to voluntarily participate and answer questions of the “Graph Literacy Scaleâ€. The study takes place during the annual progress test, a obligatory test students of all study years have to take.</p>
</section>
<section id="outcome-measures" class="level3">
<h3 class="anchored" data-anchor-id="outcome-measures">Outcome Measures</h3>
-<p>We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information. The students had to answer 9 open and 4 multiple choice questions. Each of the questions represents one of the 3 levels of graph comprehension: reading the data, reading between the data, reading beyond the data (Galesic and Garcia-Retamero, 2011). Except for question 7 there is only one correct answer to every question. Question 7 does accept a range (>23 to >25) of correct answers. Participants received one point for every correct solution, meaning they could achieve 0 to 13 points in total. Only the correct answer was taken into account and only the defined exact numbers were considered correct in open questions. There was no error margin, questionnaires including blank responses were not considered for our analysis.</p>
+<p>We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information. The students had to answer 9 open and 4 multiple choice questions. Each of the questions represents one of the 3 levels of graph comprehension: reading the data, reading between the data, reading beyond the data (Galesic and Garcia-Retamero, 2011). Except for question 7 there is only one correct answer to every question. Question 7 does accept a range (>23 to >25) of correct answers. Participants received one point for every correct solution, meaning they could achieve 0 to 13 points in total. Only the correct answer was taken into account and only the defined exact numbers were considered correct in open questions. There was no error margin, questionnaires including blank responses were not considered for our analysis. The average score (standard deviation) in this test format according to Galesic and Garcia-Retamero (2011) is 9.4 (standard deviation 2.6) for Germany.</p>
</section>
<section id="statistical-methods" class="level3">
<h3 class="anchored" data-anchor-id="statistical-methods">Statistical methods</h3>
-<p>…</p>
-<button class="btn btn-default btn-webr" disabled="" type="button" id="webr-run-button-2">Loading
+<p>All data were entered and alyzed using the statistial software R Studio 2023.12.1. Descriptive means and standard deviations were calculated for participants’ age and mean test scores were computed for all participants’ and for each study year seperately.</p>
+<button class="btn btn-default btn-webr" disabled="" type="button" id="webr-run-button-1">Loading
webR...</button>
-<div id="webr-editor-2"></div>
-<div id="webr-code-output-2" aria-live="assertive">
+<div id="webr-editor-1"></div>
+<div id="webr-code-output-1" aria-live="assertive">
<pre style="visibility: hidden"></pre>
</div>
<script type="module">
// Retrieve webR code cell information
- const runButton = document.getElementById("webr-run-button-2");
- const outputDiv = document.getElementById("webr-code-output-2");
- const editorDiv = document.getElementById("webr-editor-2");
+ const runButton = document.getElementById("webr-run-button-1");
+ const outputDiv = document.getElementById("webr-code-output-1");
+ const editorDiv = document.getElementById("webr-editor-1");
// Add a light grey outline around the code editor
editorDiv.style.border = "1px solid #eee";
@@ -690,64 +670,51 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
<h2 class="anchored" data-anchor-id="results">Results</h2>
<section id="recruitment-process-and-demographic-characteristics" class="level3">
<h3 class="anchored" data-anchor-id="recruitment-process-and-demographic-characteristics">Recruitment Process and Demographic Characteristics</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 {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
+<p>We obtained 449 complete questionnaires, which could be included in our analysis. Out of a total of 500 questionnaires 31 surveys were actively canceled by the respondents and therefore not considered. Out of those 469 left over, 20 were lacking answers regarding individual responses and therefor haven’t been included. 2 questionnaires were lacking information about the sex of the respondents, 1 was lacking information regarding age, those 3 weren’t excluded. Out of our 449 included questionnaires 126 students were in their first academic year, 128 students in their second academic year, 54 students in their third year, 59 students in their fourth year and 82 students in their fifth year. 151 students stated their sex was male, 296 stated their sex was female, 2 didn’t give informations regarding sex.</p>
</section>
<section id="primary-and-secondary-outcomes" class="level3">
<h3 class="anchored" data-anchor-id="primary-and-secondary-outcomes">Primary and secondary Outcomes</h3>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><a href="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="lightbox" 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 {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
+<p>After the evaluation of all datasets, three major findings were obtained. The first finding is that participants achieved an average score of 11.42 (s = 1.42) points, which is significantly higher than the average score of Galesic (2011) with 9.4 (s = 2.6) points. The second major finding is, although there is a slight increase in the scores between the first (11.33 points [s = 1.40]) to the fifth academic year (11.65 [s = 1.18]), no significant difference could be detected (p = xx). Finally, although the students had an excellent overall performance, there was one question (question 11, testing abilities to read beyond the data) achieving poor results with an increase of correct answers in the older cohorts, but still a ceiling effect on a low level with a maximum of 57,6% correct answers.</p>
</section>
</section>
<section id="discussion" class="level2 page-columns page-full">
<h2 class="anchored" data-anchor-id="discussion">Discussion</h2>
-<section id="summary" class="level3">
-<h3 class="anchored" data-anchor-id="summary">Summary</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>
-<p>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.</p>
-</section>
-<section id="limitation-study-design" class="level3">
-<h3 class="anchored" data-anchor-id="limitation-study-design">Limitation: study design</h3>
-<p>…</p>
-</section>
+<p>The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent. Fluctuation range in the area of reading beyond the data is extremely high between individual questions. A finding also seen in the validation study (Galesic, 2011). Although our medical students perform better than the reference group, the overall result for question 11 is poor with 38.1%-57.3% (mean 48,3%) correct answers. Although there is an improvement in the ability to answer the question across the different cohorts with each year of study, there is a certain ceiling effect. Question 11 requires the comparison between 2 line charts. The participants are asked to decide based on those line charts which treatment contributes to a larger decrease of sick patients. The y-axes in those charts state “% sick patients†and does not give informations regarding the scale. The correct answer would be “can’t say†based on the charts. <span class="citation" data-cites="Garcia2011">[<a href="#ref-Garcia2011" role="doc-biblioref"><strong>Garcia2011?</strong></a>]</span></p>
+<p>Scoring high in other aspects of reading beyond the data, a possible explanation for poor performance of our medical students could be a base rate neglect manifesting with poor results when answering question 11. Whenever the correct answer is “can’t say†also a lacking tolerance of ambiguity could be an explanation for a poor performance. Considering the role of physicians giving answers and guiding therapy, admitting one can’t give an answer, might be especially complex. Low aversion to ambiguity is a known risk factor for suboptimal decision-making in clinical practice, as for example is shown by <span class="citation" data-cites="Saposnik2011">[<a href="#ref-Saposnik2011" role="doc-biblioref"><strong>Saposnik2011?</strong></a>]</span> for treatment in patients with Spinal Muscular Atrophy (SMA). If the assumption that a lack of ambiguity tolerance contributes to the response behavior is correct, this has far-reaching consequences for the need for training of the low graph literacy shown in our medical students cohort.</p>
+<p>There are some limitations to our study, that need to be acknowledged. First, we performed a cross-sectional study, that was carried out on a specific point in time and in the setting of an mandatory test, every medical student has to take. This might have caused students to care less for correct answers or having a lower attention span because they already had to spend severe effort to perform well in their test. We can’t say, if participants might have performed much better at a different time or at a later stage of their training, for example as residents in clinical practice. There might be an training effect for graph literacy, that only evoles over time needing to read and explain data regularly to patients or colleagues. Further we didn’t ask participants to give specific informations regarding their statistical training in school or during their medical studies. We also can’t say why the performance regarding graph literacy was especially inconsistent in “reading beyond the dataâ€. This question does need further research attention. Nonetheless, a cross-sectional study design remains an efficient way to evaluate the prevalence of certain skills in a large sample of students. A true statement about how the graph literacy changes can not be given, because we did a single time measurement. But we can say, that in comparisson those students of higher academic experience do perform better when measuring their graph literacy.</p>
<section id="integration-with-prior-work" class="level3 page-columns page-full">
<h3 class="anchored" data-anchor-id="integration-with-prior-work">Integration with prior work</h3>
<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">9</a>]</span>.</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">9</a>]</span>. This is consistent with our research findings, which also revealed high overall graph literacy skills for medical students. Due to the very good results of the students at the level of data reading skills, the authors of the study recommend a reduction of the graph literacy measurement for medical students to the aspects of reading between and beyond the data. Assuming this could lead to a more objective measurement of graphical skills in trained persons. However, since only a precise record of all aspects of the abilities and weaknesses of medical students provides a comprehensive picture, this abbreviation does not appear to be adequate for research purposes.</p>
<div class="no-row-height column-margin column-container"><div id="ref-mas2018graphical" class="csl-entry" role="listitem">
9. 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 id="ref-Nayak2016" class="csl-entry" role="listitem">
-10. Nayak JG, Hartzler AL, Macleod LC, Izard JP, Dalkin BM, Gore JL. <a href="https://doi.org/10.1016/j.pec.2015.09.009">Relevance of graph literacy in the development of patient-centered communication tools</a>. Patient Education and Counseling. 2016;99:448–54.
-</div></div><p><span class="citation" data-cites="Nayak2016">[<a href="#ref-Nayak2016" role="doc-biblioref">10</a>]</span> highlights the importance of graph literacy in patient-centered communication tools and its correlation with dashboard comprehension.</p>
-<p><span class="citation" data-cites="Gaissmaier2012">[<a href="#ref-Gaissmaier2012" role="doc-biblioref">11</a>]</span> emphasizes the significance of graph literacy in understanding health-related statistical information and its relationship with comprehension and recall.</p>
-<div class="no-row-height column-margin column-container"><div id="ref-Gaissmaier2012" class="csl-entry" role="listitem">
-11. Gaissmaier W, Wegwarth O, Skopec D, Müller A-S, Broschinski S, Politi MC. <a href="https://doi.org/10.1037/a0024850">Numbers can be worth a thousand pictures: Individual differences in understanding graphical and numerical representations of health-related information.</a> Health Psychology. 2012;31:286–96.
</div><div id="ref-Durand2020" class="csl-entry" role="listitem">
-12. Durand M-A, Yen RW, O’Malley J, Elwyn G, Mancini J. <a href="https://doi.org/10.1371/journal.pone.0241844">Graph literacy matters: Examining the association between graph literacy, health literacy, and numeracy in a Medicaid eligible population</a>. PLOS ONE. 2020;15:e0241844.
-</div></div><p><span class="citation" data-cites="Durand2020">[<a href="#ref-Durand2020" role="doc-biblioref">12</a>]</span> examines the relationship between graph literacy, numeracy, health literacy, and sociodemographic characteristics in a Medicaid-eligible population.</p>
+10. Durand M-A, Yen RW, O’Malley J, Elwyn G, Mancini J. <a href="https://doi.org/10.1371/journal.pone.0241844">Graph literacy matters: Examining the association between graph literacy, health literacy, and numeracy in a Medicaid eligible population</a>. PLOS ONE. 2020;15:e0241844.
+</div><div id="ref-Gaissmaier2012" class="csl-entry" role="listitem">
+11. Gaissmaier W, Wegwarth O, Skopec D, Müller A-S, Broschinski S, Politi MC. <a href="https://doi.org/10.1037/a0024850">Numbers can be worth a thousand pictures: Individual differences in understanding graphical and numerical representations of health-related information.</a> Health Psychology. 2012;31:286–96.
+</div><div id="ref-Nayak2016" class="csl-entry" role="listitem">
+12. Nayak JG, Hartzler AL, Macleod LC, Izard JP, Dalkin BM, Gore JL. <a href="https://doi.org/10.1016/j.pec.2015.09.009">Relevance of graph literacy in the development of patient-centered communication tools</a>. Patient Education and Counseling. 2016;99:448–54.
+</div></div><p>Literature regarding patients’ and nurses’ graphy literacy does provide further insight with respect to the different levels of graph literacy. For example Durand et al <span class="citation" data-cites="Durand2020">[<a href="#ref-Durand2020" role="doc-biblioref">10</a>]</span> examine the relationship between graph literacy, numeracy, health literacy, and sociodemographic characteristics in a Medicai-eligible population. They did not find a statistically significant association betwwen Graph literacy and higher education. But they found a positive relationship between graph literacy and numeracy, while numeracy was associated to education. <span class="citation" data-cites="Gaissmaier2012">[<a href="#ref-Gaissmaier2012" role="doc-biblioref">11</a>]</span> emphasizes the significance of graph literacy in understanding health-related statistical information and ability for informed medical decision making. They found a high graph literacy to be associated with a better comprehension and recall for graphics instead of numbers. This makes graph literacy tremendous important in patient-centered communication tools, as shown by <span class="citation" data-cites="Nayak2016">[<a href="#ref-Nayak2016" role="doc-biblioref">12</a>]</span>. <span class="citation" data-cites="Dowding2018">[<a href="#ref-Dowding2018" role="doc-biblioref"><strong>Dowding2018?</strong></a>]</span> do support this statement for visual displays for clinical dashboards used by nurses.</p>
+<p><span class="citation" data-cites="Gaissmeier2023">[<a href="#ref-Gaissmeier2023" role="doc-biblioref"><strong>Gaissmeier2023?</strong></a>]</span> people fail to identify what they comprehend better but base their choice between representations on how attractive the representation is.</p>
<p><span class="citation" data-cites="Tolbert2018">[<a href="#ref-Tolbert2018" role="doc-biblioref">13</a>]</span> discusses the impact of different graphical representations on the accurate interpretation and clarity of patient-reported outcome scores in cancer patients/survivors.</p>
<div class="no-row-height column-margin column-container"><div id="ref-Tolbert2018" class="csl-entry" role="listitem">
13. Tolbert E, Brundage M, Bantug E, Blackford AL, Smith K, Snyder C. <a href="https://doi.org/10.1177/0272989x18791177">Picture This: Presenting Longitudinal Patient-Reported Outcome Research Study Results to Patients</a>. Medical Decision Making. 2018;38:994–1005.
</div></div></section>
<section id="implications-for-practice" class="level3">
<h3 class="anchored" data-anchor-id="implications-for-practice">Implications for practice</h3>
-<p>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.</p>
+<p>Despite some improvement across different cohorts with each year of study, there seems to be a ceiling effect in abilities for reading beyond the data. This suggests that even with progression through medical education, there are limitations in the learning ability for this particular item. A possible base rate neglect and a lacking tolerance of ambiguity as explanations for poor performance, particularly when the correct answer is “can’t say,†adds a psychological dimension to the interpretation of our results. The reluctance to admit uncertainty, especially in a medical context where physicians are expected to provide answers and guide therapy, could be contributing to this specific challenge. For future training of medical students, addressing these challenges could involve interventions to improve tolerance for ambiguity and emphasize the importance of acknowledging uncertainty when necessary. Training programs might incorporate exercises that specifically target those skills. Additionally, providing awareness and education about base rate neglect could enhance decision-making in situations where uncertainty is inherent. Further it can be deduced that special attention in graph literacy of medical students should also be paid to the ability to communicate visually presented results. This takes into account the doctor as a mediator between specialist knowledge and patient interests, as well as the possibly lower graph literacy skills of patients.</p>
</section>
<section id="implications-for-research" class="level3">
<h3 class="anchored" data-anchor-id="implications-for-research">Implications for research</h3>
-<p>…</p>
+<p>Our findings suggest that while medical students generally exhibit above-average results in graph literacy, particularly in reading and interpreting data, there are challenges in their ability to go beyond the presented data. This fluctuations in performance does indicate a need for future research. Exploring the psychological factors influencing graph literacy in medical students, such as tolerance of ambiguity, could be valuable to gain a better understanding. Investigating interventions that could improve performance in reading beyond the data, especially in situations involving uncertainty, could contribute to the development of more effective educational strategies.</p>
</section>
<section id="conclusions" class="level3">
<h3 class="anchored" data-anchor-id="conclusions">Conclusions</h3>
-<p>…</p>
+<p>In summary, our findings suggest a nuanced understanding of the strengths and challenges in the graph literacy of medical students, with implications for both their training and need of further research in this domain.</p>
+</section>
+<section id="acknowledgements" class="level3">
+<h3 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h3>
+<p>We thank Mirta Galesic and Rocio Garcia-Retamero for providing their Graph Literacy scale (pen and paper format). This study had no external funding.</p>
</section>
</section>
<section id="references" class="level2 unnumbered">
@@ -760,7 +727,7 @@ 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>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>
+<p>Approval was obtained from the local Ethics Committee University Muenster, Germany. A validated questionnaire for “Graph Literacy†was handed out to medical students during April 2017 to June 2017. Completion of the questionnaire was voluntary.</p>
</section>
<section id="consent-for-publication" class="level3">
<h3 class="anchored" data-anchor-id="consent-for-publication">Consent for publication</h3>
@@ -780,7 +747,7 @@ 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>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.</p>
+<p>We thank Mirta Galesic and Rocio Garcia-Retamero for providing their Graph Literacy scale (pen and paper format).</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>
@@ -791,14 +758,14 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
<h3 class="anchored" data-anchor-id="acknowledgments">Acknowledgments</h3>
<p>The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Ziel-Journal. 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>
</section>
-</div></section><section class="quarto-appendix-contents" id="quarto-reuse"><h2 class="anchored quarto-appendix-heading">Reuse</h2><div class="quarto-appendix-contents"><div><a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a></div></div></section><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{soler wenglein2024,
+</div></section><section class="quarto-appendix-contents" id="quarto-reuse"><h2 class="anchored quarto-appendix-heading">Reuse</h2><div class="quarto-appendix-contents"><div><a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a></div></div></section><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{soler_wenglein2024,
author = {Janina Soler Wenglein and Hendrik Friederichs},
title = {Assessment of Graph Literacy Among {German} Medical Students
-\/- a Cross-Sectional Study to Assess Graph Interpretation Skills},
date = {2024-02-14},
langid = {en}
}
-</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-soler wenglein2024" class="csl-entry quarto-appendix-citeas" role="listitem">
+</code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-soler_wenglein2024" class="csl-entry quarto-appendix-citeas" role="listitem">
1. Janina Soler Wenglein, Hendrik Friederichs. Assessment of graph
literacy among German medical students -- a cross-sectional study to
assess graph interpretation skills. Ziel-Journal. 2024.
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diff --git a/text/_abstract.md b/text/_abstract.md
index f2f340c..b27a9cd 100644
--- a/text/_abstract.md
+++ b/text/_abstract.md
@@ -3,6 +3,6 @@
**Methods**: A cross-sectional study was performed with medical students in their academic years 1, 2, 3, 4 and 5 at the medical school of the University of Muenster, Germany. We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information.
-**Results**: {{< lipsum 1 >}}
+**Results**: The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although the students had an excellent overall performance, there was one question (question 11) achieving poor results with an better performance in higher academic years but still a ceiling effect on a low level with a maximum of 57,6% correct answers.
-**Conclusio**: The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent.
\ No newline at end of file
+**Conclusion**: Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent. This leads to implications for training of medical students and need of further research in this domain.
\ No newline at end of file
diff --git a/text/_declarations.md b/text/_declarations.md
index c05c257..fcd3285 100644
--- a/text/_declarations.md
+++ b/text/_declarations.md
@@ -1,7 +1,7 @@
### Ethics approval and consent to participate
-{{< lipsum 1 >}}
+Approval was obtained from the local Ethics Committee University Muenster, Germany. A validated questionnaire for "Graph Literacy" was handed out to medical students during April 2017 to June 2017. Completion of the questionnaire was voluntary.
### Consent for publication
@@ -20,8 +20,7 @@ The authors declare that they have no competing interests.
The author(s) received no specific funding for this work.
### Authors' contributions
-
-{{< lipsum 1 >}}
+We thank Mirta Galesic and Rocio Garcia-Retamero for providing their Graph Literacy scale (pen and paper format).
### CRediT authorship contribution statement
diff --git a/text/_imrad.md b/text/_imrad.md
index 1c5d2f4..ca97faf 100644
--- a/text/_imrad.md
+++ b/text/_imrad.md
@@ -2,15 +2,15 @@
### 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.
+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, interpreting of epidemiological data and communicating in medical teams.
### Theoretical and/or 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.
+One important aspect of health literacy is graph literacy, meaning the reading, interpreting and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and is known as semiotic activity. When informations are provided graphically the underlying assumption, this could reduce the cognitive work load and would be easier to understand, seems obvious. Thus, the ability to understand graphs should not be considered isolated 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 [@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 [@cooper2002graphical]. 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 [@melnik2023my].
-In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy [@roth2002reading]. It can have an impact on risk comprehension [@okan2019using], suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients [@durand2020graph] or the ability of doctors [@caverly2015doctors] to interpret graphical representations.
+In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy [@roth2002reading]. It can have an impact on risk comprehension [@okan2019using], suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients [@durand2020graph] or the ability of doctors [@caverly2015doctors] to interpret graphical representations. Data regarding medical students ability of graph literacy and investigations on how to increase these are lacking.
### Focused problem statement
@@ -22,8 +22,9 @@ When providing information to patients, medical doctors must be aware about pati
We performed a study of medical students to investigate the following questions:
-1. What is ...
-2. Why are ...
+1. How high is the graph literacy among medical students?
+2. Does the graph literacy of medical students change during their respective academic progress (measured in years)?
+3. Are there any sub-areas of medical students' graph literacy that require special attention?
## Methods
@@ -41,30 +42,16 @@ Approval was obtained from the local Ethics Committee University Muenster, Germa
### 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")
-```
+Data collection for this study was determined à priori as follows: students of each study year of the medical University Muenster are asked to voluntarily participate and answer questions of the "Graph Literacy Scale". The study takes place during the annual progress test, a obligatory test students of all study years have to take.
### Outcome Measures
-We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information. The students had to answer 9 open and 4 multiple choice questions. Each of the questions represents one of the 3 levels of graph comprehension: reading the data, reading between the data, reading beyond the data (Galesic and Garcia-Retamero, 2011). Except for question 7 there is only one correct answer to every question. Question 7 does accept a range (\>23 to \>25) of correct answers. Participants received one point for every correct solution, meaning they could achieve 0 to 13 points in total. Only the correct answer was taken into account and only the defined exact numbers were considered correct in open questions. There was no error margin, questionnaires including blank responses were not considered for our analysis.
+We did measure outcome using the “Graph Literacy Scale†(Galesic and Garcia-Retamero, 2011). This test measures the graph literacy, meaning to read and interpret graphical provided information. The students had to answer 9 open and 4 multiple choice questions. Each of the questions represents one of the 3 levels of graph comprehension: reading the data, reading between the data, reading beyond the data (Galesic and Garcia-Retamero, 2011). Except for question 7 there is only one correct answer to every question. Question 7 does accept a range (\>23 to \>25) of correct answers. Participants received one point for every correct solution, meaning they could achieve 0 to 13 points in total. Only the correct answer was taken into account and only the defined exact numbers were considered correct in open questions. There was no error margin, questionnaires including blank responses were not considered for our analysis. The average score (standard
+deviation) in this test format according to Galesic and Garcia-Retamero (2011) is 9.4 (standard deviation 2.6) for Germany.
### Statistical methods
-...
+All data were entered and alyzed using the statistial software R Studio 2023.12.1. Descriptive means and standard deviations were calculated for participants' age and mean test scores were computed for all participants' and for each study year seperately.
```{webr-r}
#| context: interactive
@@ -91,53 +78,47 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
### Recruitment Process and Demographic Characteristics
-The recruitment process is shown in Figure 1. We obtained 449 complete questionnaires, which could be included in our analysis. Out of a total of 500 questionnaires 31 surveys were actively canceled by the respondents and therefore not considered. Out of those 469 left over, 20 were lacking answers regarding individual responses and therefor haven’t been included. 2 questionnaires were lacking information about the sex of the respondents, 1 was lacking information regarding age but weren’t excluded. Out of our 449 included questionnaires 126 students were in their first academic year, 128 students in their second academic year, 54 students in their third year, 59 students in their fourth year and 82 students in their fifth year.
-
-<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
+We obtained 449 complete questionnaires, which could be included in our analysis. Out of a total of 500 questionnaires 31 surveys were actively canceled by the respondents and therefore not considered. Out of those 469 left over, 20 were lacking answers regarding individual responses and therefor haven’t been included. 2 questionnaires were lacking information about the sex of the respondents, 1 was lacking information regarding age, those 3 weren’t excluded. Out of our 449 included questionnaires 126 students were in their first academic year, 128 students in their second academic year, 54 students in their third year, 59 students in their fourth year and 82 students in their fifth year. 151 students stated their sex was male, 296 stated their sex was female, 2 didn't give informations regarding sex.
### Primary and secondary Outcomes
-
-
-<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
+After the evaluation of all datasets, three major findings were obtained. The first finding is that participants achieved an average score of 11.42 (s = 1.42) points, which is significantly higher than the average score of Galesic (2011) with 9.4 (s = 2.6) points. The second major finding is, although there is a slight increase in the scores between the first (11.33 points [s = 1.40]) to the fifth academic year (11.65 [s = 1.18]), no significant difference could be detected (p = xx). Finally, although the students had an excellent overall performance, there was one question (question 11, testing abilities to read beyond the data) achieving poor results with an increase of correct answers in the older cohorts, but still a ceiling effect on a low level with a maximum of 57,6% correct answers.
## Discussion
-### Summary
-
-After the evaluation of all datasets, the following findings emerged. The first is that ...
+The first major finding of our study is that medical students showed above average test results in the “Graph Literacy Scale†in terms of their graph literacy compared to a German standard population. Although abilities in reading the data and between the data are high in our cohorts, abilities to read beyond the data are inconsistent. Fluctuation range in the area of reading beyond the data is extremely high between individual questions. A finding also seen in the validation study (Galesic, 2011). Although our medical students perform better than the reference group, the overall result for question 11 is poor with 38.1%-57.3% (mean 48,3%) correct answers. Although there is an improvement in the ability to answer the question across the different cohorts with each year of study, there is a certain ceiling effect. Question 11 requires the comparison between 2 line charts. The participants are asked to decide based on those line charts which treatment contributes to a larger decrease of sick patients. The y-axes in those charts state "% sick patients" and does not give informations regarding the scale. The correct answer would be "can't say" based on the charts. @Garcia2011
-### Limitation: study population
+Scoring high in other aspects of reading beyond the data, a possible explanation for poor performance of our medical students could be a base rate neglect manifesting with poor results when answering question 11. Whenever the correct answer is “can’t say†also a lacking tolerance of ambiguity could be an explanation for a poor performance. Considering the role of physicians giving answers and guiding therapy, admitting one can’t give an answer, might be especially complex. Low aversion to ambiguity is a known risk factor for suboptimal decision-making in clinical practice, as for example is shown by @Saposnik2011 for treatment in patients with Spinal Muscular Atrophy (SMA). If the assumption that a lack of ambiguity tolerance contributes to the response behavior is correct, this has far-reaching consequences for the need for training of the low graph literacy shown in our medical students cohort.
-{{< lipsum 1 >}}
-
-### Limitation: study design
-
-...
+There are some limitations to our study, that need to be acknowledged. First, we performed a cross-sectional study, that was carried out on a specific point in time and in the setting of an mandatory test, every medical student has to take. This might have caused students to care less for correct answers or having a lower attention span because they already had to spend severe effort to perform well in their test. We can't say, if participants might have performed much better at a different time or at a later stage of their training, for example as residents in clinical practice. There might be an training effect for graph literacy, that only evoles over time needing to read and explain data regularly to patients or colleagues. Further we didn't ask participants to give specific informations regarding their statistical training in school or during their medical studies. We also can't say why the performance regarding graph literacy was especially inconsistent in "reading beyond the data". This question does need further research attention.
+Nonetheless, a cross-sectional study design remains an efficient way to evaluate the prevalence of certain skills in a large sample of students. A true statement about how the graph literacy changes can not be given, because we did a single time measurement. But we can say, that in comparisson those students of higher academic experience do perform better when measuring their graph literacy.
### 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].
+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]. This is consistent with our research findings, which also revealed high overall graph literacy skills for medical students. Due to the very good results of the students at the level of data reading skills, the authors of the study recommend a reduction of the graph literacy measurement for medical students to the aspects of reading between and beyond the data. Assuming this could lead to a more objective measurement of graphical skills in trained persons. However, since only a precise record of all aspects of the abilities and weaknesses of medical students provides a comprehensive picture, this abbreviation does not appear to be adequate for research purposes.
-@Nayak2016 highlights the importance of graph literacy in patient-centered communication tools and its correlation with dashboard comprehension.
+Literature regarding patients' and nurses' graphy literacy does provide further insight with respect to the different levels of graph literacy. For example Durand et al @Durand2020 examine the relationship between graph literacy, numeracy, health literacy, and sociodemographic characteristics in a Medicai-eligible population. They did not find a statistically significant association betwwen Graph literacy and higher education. But they found a positive relationship between graph literacy and numeracy, while numeracy was associated to education. @Gaissmaier2012 emphasizes the significance of graph literacy in understanding health-related statistical information and ability for informed medical decision making. They found a high graph literacy to be associated with a better comprehension and recall for graphics instead of numbers. This makes graph literacy tremendous important in patient-centered communication tools, as shown by @Nayak2016. @Dowding2018 do support this statement for visual displays for clinical dashboards used by nurses.
-@Gaissmaier2012 emphasizes the significance of graph literacy in understanding health-related statistical information and its relationship with comprehension and recall.
-
-@Durand2020 examines the relationship between graph literacy, numeracy, health literacy, and sociodemographic characteristics in a Medicaid-eligible population.
+@Gaissmeier2023 people fail to identify what they comprehend better but base their choice between representations on how attractive the representation is.
@Tolbert2018 discusses the impact of different graphical representations on the accurate interpretation and clarity of patient-reported outcome scores in cancer patients/survivors.
### Implications for practice
-{{< lipsum 1 >}}
+Despite some improvement across different cohorts with each year of study, there seems to be a ceiling effect in abilities for reading beyond the data. This suggests that even with progression through medical education, there are limitations in the learning ability for this particular item. A possible base rate neglect and a lacking tolerance of ambiguity as explanations for poor performance, particularly when the correct answer is "can't say," adds a psychological dimension to the interpretation of our results. The reluctance to admit uncertainty, especially in a medical context where physicians are expected to provide answers and guide therapy, could be contributing to this specific challenge.
+For future training of medical students, addressing these challenges could involve interventions to improve tolerance for ambiguity and emphasize the importance of acknowledging uncertainty when necessary. Training programs might incorporate exercises that specifically target those skills. Additionally, providing awareness and education about base rate neglect could enhance decision-making in situations where uncertainty is inherent.
+Further it can be deduced that special attention in graph literacy of medical students should also be paid to the ability to communicate visually presented results. This takes into account the doctor as a mediator between specialist knowledge and patient interests, as well as the possibly lower graph literacy skills of patients.
### Implications for research
-...
+Our findings suggest that while medical students generally exhibit above-average results in graph literacy, particularly in reading and interpreting data, there are challenges in their ability to go beyond the presented data. This fluctuations in performance does indicate a need for future research. Exploring the psychological factors influencing graph literacy in medical students, such as tolerance of ambiguity, could be valuable to gain a better understanding. Investigating interventions that could improve performance in reading beyond the data, especially in situations involving uncertainty, could contribute to the development of more effective educational strategies.
### Conclusions
-...
+In summary, our findings suggest a nuanced understanding of the strengths and challenges in the graph literacy of medical students, with implications for both their training and need of further research in this domain.
+
+### Acknowledgements
+We thank Mirta Galesic and Rocio Garcia-Retamero for providing their Graph Literacy scale (pen and paper format). This study had no external funding.
diff --git a/text/_plain-language-summary.md b/text/_plain-language-summary.md
index 0c7c3fe..83db1f8 100644
--- a/text/_plain-language-summary.md
+++ b/text/_plain-language-summary.md
@@ -2,18 +2,18 @@
plain-language-summary: |
**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.
- **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.
+ **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. Die Annahme grafische Darstellungen würden grundsätzlich zum vereinfachten Verständnis von Informationen beitragen, ist irreführend. Dies ist besonders relevant in Bereichen wie der medizinischen Forschung, wo Grafiken zur Vermittlung komplexer Informationen genutzt werden. Fehlerhafte rezipierte Darstellungen können ebenso wie Fehldarstellungen die Informationsaufnahme beeinflussen.
**Gap des Problems:**
<!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität -->
- Graph Literacy beeinflusst das Risikoverständnis und Entscheidungsverhalten, wobei Studien hauptsächlich Patienten und Ärzte im Umgang mit grafischen Darstellungen betrachten.
+ Graph Literacy beeinflusst das Risikoverständnis und Entscheidungsverhalten, wobei Studien hauptsächlich Patienten und Ärzte im Umgang mit grafischen Darstellungen betrachten. Zur Graph Literacy bei Medizinstudierenden sind bislang wenig Forschungsergebnisse publiziert, so dass auch ein tieferes Verständnis davon, wie die Graph Literacy im Medizinstudium gestärkt werden kann, fehlt.
**Lösung?:**
<!-- möglicher Fortschritt / mögliche Lösung - Ansprechen von Motiv(en): Anschluss, Leistung, Macht -->
Für Ärzte, die Menschen mit geringerer Gesundheitskompetenz betreuen, ist es wichtig, selbst eine hohe Kompetenz im Verständnis visueller Darstellungen zu haben. Diese Fertigkeit sollte schon im Medizinstudium erworben werden.
**Forschungsfragen:**
- Daher wurde eine Studie mit Medizinstudierenden durchgeführt, um ihre Fähigkeit zur Interpretation visuell dargestellter medizinischer Informationen zu untersuchen.
+ Daher wurde eine Studie mit Medizinstudierenden durchgeführt, um ihre Fähigkeit zur Interpretation visuell dargestellter medizinischer Informationen zu untersuchen.
**Studienpopulation:** Medizinstudierende
@@ -23,7 +23,7 @@ plain-language-summary: |
**Ergebnisparameter:** Anzahl der richtigen Antworten insgesamt.
- **Statistik:** Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen im Studienverlauf. Evtl. noch weitere Vergleichsberechnungen.
+ **Statistik:** Bestimmung der Prozentwerte für die absolute und z-Scores und Perzentilen für die relative Bewertung der Leistungen im Vergleich der Kohorten unterschiedlicher Studienjahre.
--
GitLab