From 5663bae2d74205dac72fc3b3f0cf618405bbadf9 Mon Sep 17 00:00:00 2001
From: Hendrik Friederichs <hendrik.friederichs@uni-bielefeld.de>
Date: Tue, 23 Jan 2024 13:14:16 +0100
Subject: [PATCH] Update 23-01-2024
---
README.md | 4 +-
_freeze/index/execute-results/html.json | 4 +-
_plain-language-summary.md | 7 +-
index.qmd | 174 ++----------------------
public/index-preview.html | 30 ++--
public/index.embed.ipynb | 99 +++++---------
public/index.html | 52 +++----
public/index.out.ipynb | 101 +++++---------
public/index.qmd | 27 +---
text/_abstract.md | 8 ++
text/_background.md | 25 ++++
text/_declarations.md | 33 +++++
text/_discussion.md | 31 +++++
text/_methods.md | 65 +++++++++
text/_results.md | 11 ++
15 files changed, 298 insertions(+), 373 deletions(-)
create mode 100644 text/_abstract.md
create mode 100644 text/_background.md
create mode 100644 text/_declarations.md
create mode 100644 text/_discussion.md
create mode 100644 text/_methods.md
create mode 100644 text/_results.md
diff --git a/README.md b/README.md
index 2544f52..cc12f0f 100644
--- a/README.md
+++ b/README.md
@@ -4,4 +4,6 @@ Graph literacy, the ability to understand and interpret graphical data, is a cru
Graph literacy is not just an academic requirement but a practical necessity in the medical profession. It enables medical students and professionals to interpret and utilize data effectively, which is fundamental to providing high-quality patient care and advancing medical knowledge. Notably, existing research on graph literacy primarily focuses on patients or the capability of doctors to decipher graphical representations.
-This study is designed to find out how well medical students can understand and use graphs. We aim to learn how extensive these skills are and how they grow.
\ No newline at end of file
+This study is designed to find out how well medical students can understand and use graphs. We aim to learn how extensive these skills are and how they grow.
+
+01-2024
\ No newline at end of file
diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json
index 302658e..e1b8eb1 100644
--- a/_freeze/index/execute-results/html.json
+++ b/_freeze/index/execute-results/html.json
@@ -1,8 +1,8 @@
{
- "hash": "d8732ddcda7700ffc21b497e420d3fd5",
+ "hash": "2e238aa8edaaf7aea1cfec82ab7a5863",
"result": {
"engine": "knitr",
- "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Medical Education\n - Artificial Intelligence\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n**Background / Hintergrund**: ...\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background / Hintergrund\n\n### Broad problem / Allgemeineres 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 / Theoretische und/oder empirische Fokussierung des Problems\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt\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 study was conducted at Medical Faculty of Münster ...\n\nIt takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year). ...\n\n### Study design / Studiendesign\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\nAll participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n- Input ...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\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 / Ergebnisparameter\n\n...\n\n### Statistical methods / Statistische Methoden\n\nWe used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) [@hattie2023visible] in outcome level between intervention and control groups (calculated *a priori* with G\\*Power 3.1) [@faul2007g]. Statistical analysis, tables and figures were conducted using R [@R-base] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages [@tidyverse; @gt; @patil2021visualizations]. Descriptive means and standard deviations were calculated for participants' age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. ...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # <1>\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # <2>\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # <3>\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species), # <3>\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\")) # <3>\n```\n\n1. Download the dataset\n2. Read the data\n3. Build a scatterplot\n\n## Results / Ergebnisse\n\n### Recruitment Process and Demographic Characteristics / Studienteilnahme\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 / Haupt- und Nebenergebnisse\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n## Discussion / Diskussion\n\n### Summary / Zusammenfassung der Ergebnisse\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n### Limitation: study ndesign\n\n### Integration with prior work\n\n...\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n...\n\n### Implications for research\n\n...\n\n## Conclusions\n\n...\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n### Ethics approval and consent to participate\n\nParticipants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n\n### Consent for publication\n\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 / Konkurrierende Interessen\n\nThe authors declare that they have no competing interests.\n\n### Funding / Finanzierung\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions / Beiträge der Autor\\*innen\n\nHF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments / Danksagung\n\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",
+ "markdown": "---\ntitle: Assessment of graph literacy among German medical students -- a cross-sectional study to assess graph interpretation skills\nsubtitle: Draft of the manuscript\ntitle-block-banner: true # \"#145F7D\" als Fakultäts-Farbe\n# title-block-banner-color: \"#F0F0F0\" als weisse Schrift\ntheme:\n light: flatly\n dark: darkly\nmetadata-files: \n - _authordata.yml # vor der finalen Online-Veröffentlichung noch hierhin kopieren\n - _plain-language-summary.md # die Gedankenstruktur des Manuskripts: siehe NOTIZ\nfilters:\n - authors-block # um den Autoren-Block auch im Word-Dokument zu haben\n - abstract-section # um den Abstract im normalen Text und nicht im YAML-Header zu schreiben\n - color-text.lua # Schriftfarben\n - webr # interaktiver R-Code\nengine: knitr\nwebr: \n show-startup-message: true\n packages: ['ggplot2']\nkeywords: \n - Medical Education\n - Artificial Intelligence\ndescription: |\n Eine allgemeine Beschreibung des Projekts, das hinter dem Manuskript steht.\nkey-points:\n - Medizinische Ausbildung ist ein Querschnittsfach aus den Bereichen Medizin, Pädagogik und Psychologie.\n - Medical education is a multidisciplinary field of medicine, education, and psychology.\ndate: last-modified\nciteproc: true\nbibliography: references.bib\ncsl: bmc-medicine.csl # https://www.zotero.org/styles/bmc-medicine\ncitation-location: margin\nnumber-sections: false\nappendix-style: default\nlightbox: auto\nfunding: \n statement: \"Der/die Autor*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\"\nlang: de\neditor:\n markdown:\n canonical: true\n---\n\n\n## Abstract\n\n\n**Background / Hintergrund**: ...\n\n**Methods / Methoden**: ...\n\n**Results / Ergebnisse**: ...\n\n**Conclusio / Schlussfolgerungen**: ...\n\n\n\n------------------------------------------------------------------------\n\n::: {.callout-caution title=\"IN PROGRESS ...\"}\nThis manuscript is a work in progress. However, thank you for your interest. Please feel free to visit this web site again at a later date.\n\n[*Dieses Manuskript ist noch in Arbeit. Wir danken Ihnen jedoch für Ihr Interesse. Bitte besuchen Sie diese Website zu einem späteren Zeitpunkt noch einmal ...*]{color=\"grey\"}\n:::\n\n::: {.callout-tip title=\"STRUKTUR DES MANUSKRIPTS\" collapse=\"true\"}\n[{{< meta plain-language-summary >}}]{color=\"grey\"}\n:::\n\n## Background\n\n\n### Broad problem\n\nHealth literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n\n### Theoretical and/or empirical focus of the problem\n\nOne important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n\nProcessing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).\n\nIn summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n\n### Focused problem statement\n\nEspecially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n\nWhen providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n\n### Statement of study intent\n\nWe performed a study of medical students to investigate the following questions:\n\n1. What is ...\n2. Why are ...\n\n\n\n## Methods\n\n\n### Setting and subjects\n\nOur study was conducted at Medical Faculty of Münster ...\n\nIt takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year (\"clinical/practical\" year). ...\n\n### Study design / Studiendesign\n\nThe participants were asked to complete the graph literacy scale voluntarily and anonymously.\n\n### Ethical approval\n\nAll participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n\n### Data collection\n\nData collection for this study was determined à priori as follows:\n\n- Input ...\n\n```{webr-r}\n#| context: setup\n\n# Download a dataset\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 / Ergebnisparameter\n\n...\n\n### Statistical methods / Statistische Methoden\n\nWe used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) [@hattie2023visible] in outcome level between intervention and control groups (calculated *a priori* with G\\*Power 3.1) [@faul2007g]. Statistical analysis, tables and figures were conducted using R [@R-base] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages [@tidyverse; @gt; @patil2021visualizations]. Descriptive means and standard deviations were calculated for participants' age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. ...\n\n```{webr-r}\n#| context: interactive\n\n# Download a dataset\ndownload.file(\n 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',\n 'penguins.csv'\n) # <1>\n\n# Read the data\npenguins = read.csv(\"penguins.csv\") # <2>\n\n# Scatterplot example: penguin bill length versus bill depth\nggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # <3>\n ggplot2::geom_point(ggplot2::aes(color = species, \n shape = species), # <3>\n size = 2) +\n ggplot2::scale_color_manual(values = c(\"darkorange\",\"darkorchid\",\"cyan4\")) # <3>\n```\n\n1. Download the dataset\n2. Read the data\n3. Build a scatterplot\n\n\n\n## Results / Ergebnisse\n\n### Recruitment Process and Demographic Characteristics / Studienteilnahme\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 / Haupt- und Nebenergebnisse\n\n\n\n<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->\n\n\n\n## Discussion\n\n### Summary / Zusammenfassung der Ergebnisse\n\nAfter the evaluation of all datasets, the following findings emerged. The first is that ...\n\n### Limitation: study population\n\n...\n\n### Limitation: study design\n\n...\n\n### Integration with prior work\n\n...\n\nOnly a few studies provide insights into the graphical and numerical skills among medical students.\n\nIn a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].\n\n### Implications for practice\n\n...\n\n### Implications for research\n\n...\n\n### Conclusions\n\n...\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n\n## Declarations {.appendix}\n\n\n\n### Ethics approval and consent to participate\n\nParticipants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n\n### Consent for publication\n\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 / Konkurrierende Interessen\n\nThe authors declare that they have no competing interests.\n\n### Funding / Finanzierung\n\nThe author(s) received no specific funding for this work.\n\n### Authors' contributions / Beiträge der Autor\\*innen\n\nHF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.\n\n### CRediT authorship contribution statement\n\n**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n\n### Acknowledgments / Danksagung\n\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",
"supporting": [],
"filters": [
"rmarkdown/pagebreak.lua"
diff --git a/_plain-language-summary.md b/_plain-language-summary.md
index 1a8685b..929a5be 100644
--- a/_plain-language-summary.md
+++ b/_plain-language-summary.md
@@ -1,9 +1,8 @@
plain-language-summary: |
- **Relevantes (Studierenden-)Problem:**
- <!-- Allgemeineres Problem: mit einem „Helden“ (Studierende oder v.a. junge Ärzte), man selbst als Wegbereiter, Trainer, „Enabler“ -->
- Die Akzeptanz von dem XYZ-THEMA hat deutlich zugenommen.
- Für einen Einsatz im professionellen Bereich sind die Leistungen bisher aber allenfalls ausreichend.
+ **Relevantes Problem:**
+ Graph Literacy ist wichtig im Rahmen der Health Literacy.
+ Damit ist sie auch für die Ausbildung der Studierenden relevant.
**Fokussiertes Problem:** Studienlage zu THEMA allgemein und Medical-Education-Kontext;
Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus.
diff --git a/index.qmd b/index.qmd
index 87b47ec..b72a53f 100644
--- a/index.qmd
+++ b/index.qmd
@@ -44,13 +44,7 @@ editor:
## Abstract
-**Background / Hintergrund**: ...
-
-**Methods / Methoden**: ...
-
-**Results / Ergebnisse**: ...
-
-**Conclusio / Schlussfolgerungen**: ...
+{{< include text/_abstract.md >}}
------------------------------------------------------------------------
@@ -64,143 +58,21 @@ This manuscript is a work in progress. However, thank you for your interest. Ple
[{{< meta plain-language-summary >}}]{color="grey"}
:::
-## Background / Hintergrund
-
-### Broad problem / Allgemeineres Problem
-
-Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.
-
-### Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems
-
-One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.
-
-Processing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).
-
-In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.
+## Background
-### Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt
-
-Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.
-
-When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.
-
-### Statement of study intent
-
-We performed a study of medical students to investigate the following questions:
-
-1. What is ...
-2. Why are ...
+{{< include text/_background.md >}}
## Methods
-### Setting and subjects
-
-Our study was conducted at Medical Faculty of Münster ...
-
-It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year ("clinical/practical" year). ...
-
-### Study design / Studiendesign
-
-The participants were asked to complete the graph literacy scale voluntarily and anonymously.
-
-### Ethical approval
-
-All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).
-
-### Data collection
-
-Data collection for this study was determined à priori as follows:
-
-- Input ...
-
-```{webr-r}
-#| context: setup
-
-# Download a dataset
-download.file(
- 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
- 'penguins.csv'
-)
-
-# Read the data
-df_penguins = read.csv("penguins.csv")
-```
-
-### Outcome Measures / Ergebnisparameter
-
-...
-
-### Statistical methods / Statistische Methoden
-
-We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) [@hattie2023visible] in outcome level between intervention and control groups (calculated *a priori* with G\*Power 3.1) [@faul2007g]. Statistical analysis, tables and figures were conducted using R [@R-base] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages [@tidyverse; @gt; @patil2021visualizations]. Descriptive means and standard deviations were calculated for participants' age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. ...
-
-```{webr-r}
-#| context: interactive
-
-# Download a dataset
-download.file(
- 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
- 'penguins.csv'
-) # <1>
-
-# Read the data
-penguins = read.csv("penguins.csv") # <2>
-
-# Scatterplot example: penguin bill length versus bill depth
-ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # <3>
- ggplot2::geom_point(ggplot2::aes(color = species,
- shape = species), # <3>
- size = 2) +
- ggplot2::scale_color_manual(values = c("darkorange","darkorchid","cyan4")) # <3>
-```
-
-1. Download the dataset
-2. Read the data
-3. Build a scatterplot
+{{< include text/_methods.md >}}
## Results / Ergebnisse
-### Recruitment Process and Demographic Characteristics / Studienteilnahme
-
-The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...
-
-<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
-
-### Primary and secondary Outcomes / Haupt- und Nebenergebnisse
-
-
-
-<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
-
-## Discussion / Diskussion
-
-### Summary / Zusammenfassung der Ergebnisse
-
-After the evaluation of all datasets, the following findings emerged. The first is that ...
-
-### Limitation: study population
-
-### Limitation: study ndesign
-
-### Integration with prior work
-
-...
-
-Only a few studies provide insights into the graphical and numerical skills among medical students.
-
-In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].
-
-### Implications for practice
-
-...
+{{< include text/_results.md >}}
-### Implications for research
+## Discussion
-...
-
-## Conclusions
-
-...
+{{< include text/_discussion.md >}}
## References {.unnumbered}
@@ -209,34 +81,4 @@ In a cross-sectional, descriptive study, the researchers applied the Objective N
## Declarations {.appendix}
-### Ethics approval and consent to participate
-
-Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).
-
-### Consent for publication
-
-Not applicable
-
-### Availability of data and materials
-
-The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).
-
-### Competing interests / Konkurrierende Interessen
-
-The authors declare that they have no competing interests.
-
-### Funding / Finanzierung
-
-The author(s) received no specific funding for this work.
-
-### Authors' contributions / Beiträge der Autor\*innen
-
-HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.
-
-### CRediT authorship contribution statement
-
-**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.
-
-### Acknowledgments / Danksagung
-
-The authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.
+{{< include text/_declarations.md >}}
diff --git a/public/index-preview.html b/public/index-preview.html
index 05a58cf..1ced43f 100644
--- a/public/index-preview.html
+++ b/public/index-preview.html
@@ -403,11 +403,11 @@
<h2 id="toc-title">Inhaltsverzeichnis</h2>
<ul>
- <li><a href="#background-hintergrund" id="toc-background-hintergrund" class="nav-link active" data-scroll-target="#background-hintergrund">Background / Hintergrund</a>
+ <li><a href="#background" id="toc-background" class="nav-link active" data-scroll-target="#background">Background</a>
<ul class="collapse">
- <li><a href="#broad-problem-allgemeineres-problem" id="toc-broad-problem-allgemeineres-problem" class="nav-link" data-scroll-target="#broad-problem-allgemeineres-problem">Broad problem / Allgemeineres Problem</a></li>
- <li><a href="#theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems" id="toc-theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems" class="nav-link" data-scroll-target="#theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems">Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems</a></li>
- <li><a href="#focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt" id="toc-focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt" class="nav-link" data-scroll-target="#focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt">Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt</a></li>
+ <li><a href="#broad-problem" id="toc-broad-problem" class="nav-link" data-scroll-target="#broad-problem">Broad problem</a></li>
+ <li><a href="#theoretical-andor-empirical-focus-of-the-problem" id="toc-theoretical-andor-empirical-focus-of-the-problem" class="nav-link" data-scroll-target="#theoretical-andor-empirical-focus-of-the-problem">Theoretical and/or empirical focus of the problem</a></li>
+ <li><a href="#focused-problem-statement" id="toc-focused-problem-statement" class="nav-link" data-scroll-target="#focused-problem-statement">Focused problem statement</a></li>
<li><a href="#statement-of-study-intent" id="toc-statement-of-study-intent" class="nav-link" data-scroll-target="#statement-of-study-intent">Statement of study intent</a></li>
</ul></li>
<li><a href="#methods" id="toc-methods" class="nav-link" data-scroll-target="#methods">Methods</a>
@@ -490,7 +490,7 @@ STRUKTUR DES MANUSKRIPTS
</div>
<div id="callout-2" class="callout-2-contents callout-collapse collapse">
<div class="callout-body-container callout-body">
-<p><span style="color: grey;"><strong>Relevantes (Studierenden-)Problem:</strong> <!-- Allgemeineres Problem: mit einem „Helden“ (Studierende oder v.a. junge Ärzte), man selbst als Wegbereiter, Trainer, „Enabler“ --> Die Akzeptanz von dem XYZ-THEMA hat deutlich zugenommen. Für einen Einsatz im professionellen Bereich sind die Leistungen bisher aber allenfalls ausreichend.<br>
+<p><span style="color: grey;"><strong>Relevantes Problem:</strong> Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant.<br>
<strong>Fokussiertes Problem:</strong> Studienlage zu THEMA allgemein und Medical-Education-Kontext;<br>
Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus.<br>
<strong>Gap des Problems:</strong> <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend.<br>
@@ -506,22 +506,22 @@ Wie sieht die Leistung bei detaillierter Betrachtung der Domänen und Kompetenzl
</div>
</div>
</div>
-<section id="background-hintergrund" class="level2">
-<h2 class="anchored" data-anchor-id="background-hintergrund">Background / Hintergrund</h2>
-<section id="broad-problem-allgemeineres-problem" class="level3">
-<h3 class="anchored" data-anchor-id="broad-problem-allgemeineres-problem">Broad problem / Allgemeineres Problem</h3>
+<section id="background" class="level2">
+<h2 class="anchored" data-anchor-id="background">Background</h2>
+<section id="broad-problem" class="level3">
+<h3 class="anchored" data-anchor-id="broad-problem">Broad problem</h3>
<p>Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.</p>
</section>
-<section id="theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems" class="level3">
-<h3 class="anchored" data-anchor-id="theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems">Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems</h3>
+<section id="theoretical-andor-empirical-focus-of-the-problem" class="level3">
+<h3 class="anchored" data-anchor-id="theoretical-andor-empirical-focus-of-the-problem">Theoretical and/or empirical focus of the problem</h3>
<p>One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.</p>
<p>Processing those visual representations is essential for understanding scientific and statistical data <span class="citation" data-cites="friel2001making">[<a href="#ref-friel2001making" role="doc-biblioref">1</a>]</span> and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).</p>
<div class="no-row-height"><div id="ref-friel2001making" class="csl-entry" role="listitem">
1. Friel SN, Curcio FR, Bright GW. Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in mathematics Education. 2001;32:124–58.
</div></div><p>In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.</p>
</section>
-<section id="focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt" class="level3">
-<h3 class="anchored" data-anchor-id="focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt">Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt</h3>
+<section id="focused-problem-statement" class="level3">
+<h3 class="anchored" data-anchor-id="focused-problem-statement">Focused problem statement</h3>
<p>Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.</p>
<p>When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.</p>
</section>
@@ -789,7 +789,7 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
<h3 class="anchored" data-anchor-id="primary-and-secondary-outcomes-haupt--und-nebenergebnisse">Primary and secondary Outcomes / Haupt- und Nebenergebnisse</h3>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
-<p><a href="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="lightbox" data-gallery="quarto-lightbox-gallery-1" data-glightbox="description: .lightbox-desc-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>
+<p><a href="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="lightbox" data-glightbox="description: .lightbox-desc-1" data-gallery="quarto-lightbox-gallery-1" title="Beispielgrafik: ein Bild sagt mehr als tausend Worte …"><img src="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="img-fluid figure-img" alt="Beispielgrafik: ein Bild sagt mehr als tausend Worte …"></a></p>
<figcaption>Beispielgrafik: ein Bild sagt mehr als tausend Worte …</figcaption>
</figure>
</div>
@@ -1402,7 +1402,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
}
}
});
-</script> </div> <!-- /content --> <script>var lightboxQuarto = GLightbox({"descPosition":"bottom","openEffect":"zoom","selector":".lightbox","loop":false,"closeEffect":"zoom"});
+</script> </div> <!-- /content --> <script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","descPosition":"bottom","loop":false,"selector":".lightbox","openEffect":"zoom"});
window.onload = () => {
lightboxQuarto.on('slide_before_load', (data) => {
const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/public/index.embed.ipynb b/public/index.embed.ipynb
index 3ae081b..8385739 100644
--- a/public/index.embed.ipynb
+++ b/public/index.embed.ipynb
@@ -34,7 +34,7 @@
"\n",
"> **STRUKTUR DES MANUSKRIPTS**\n",
">\n",
- "> <span color=\"grey\">**Relevantes (Studierenden-)Problem:** <!-- Allgemeineres Problem: mit einem „Helden“ (Studierende oder v.a. junge Ärzte), man selbst als Wegbereiter, Trainer, „Enabler“ --> Die Akzeptanz von dem XYZ-THEMA hat deutlich zugenommen. Für einen Einsatz im professionellen Bereich sind die Leistungen bisher aber allenfalls ausreichend. \n",
+ "> <span color=\"grey\">**Relevantes Problem:** Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant. \n",
"> **Fokussiertes Problem:** Studienlage zu THEMA allgemein und Medical-Education-Kontext; \n",
"> Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus. \n",
"> **Gap des Problems:** <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend. \n",
@@ -48,13 +48,13 @@
"> **Ergebnisparameter:** Anzahl der richtigen Antworten insgesamt und pro Domäne bzw. Kompetenzlevel \n",
"> **Statistik:** Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen.</span>\n",
"\n",
- "## Background / Hintergrund\n",
+ "## Background\n",
"\n",
- "### Broad problem / Allgemeineres Problem\n",
+ "### Broad problem\n",
"\n",
"Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n",
"\n",
- "### Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems\n",
+ "### Theoretical and/or empirical focus of the problem\n",
"\n",
"One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n",
"\n",
@@ -62,56 +62,41 @@
"\n",
"In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n",
"\n",
- "### Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt\n",
+ "### Focused problem statement\n",
"\n",
"Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n",
"\n",
"When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n",
"\n",
- "### Statement of study intent / Fokussierte Forschungsfrage/n\n",
+ "### Statement of study intent\n",
"\n",
"We performed a study of medical students to investigate the following questions:\n",
"\n",
"1. What is …\n",
"2. Why are …\n",
"\n",
- "Wir haben eine Studie mit Medizinstudierenden durchgeführt, um die folgenden Fragen zu untersuchen:\n",
+ "## Methods\n",
"\n",
- "1. Was ist …\n",
- "2. Warum sind …\n",
+ "### Setting and subjects\n",
"\n",
- "## Methods / Methoden\n",
- "\n",
- "### Setting and subjects / Setting und Probanden\n",
- "\n",
- "Our study was conducted at Medical School …\n",
- "\n",
- "Unsere Studie wurde an der Medizinischen Fakultät der … durchgeführt.\n",
+ "Our study was conducted at Medical Faculty of Münster …\n",
"\n",
"It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students’ clinical experience, they are rotated in various hospital departments during their final year (“clinical/practical†year). …\n",
"\n",
- "Das Medizinstudium in Deutschland dauert sechs Jahre, wobei die Studierenden direkt von den weiterführenden Schulen aufgenommen werden. Das Studium gliedert sich in einen vorklinischen Teil (die ersten beiden Jahre) und einen klinischen Teil (die letzten vier Jahre). Um die klinische Erfahrung der Studenten zu verbessern, werden sie während ihres letzten Jahres (klinisch-praktisches Jahr) in verschiedenen Krankenhausabteilungen eingesetzt.\n",
- "\n",
"### Study design / Studiendesign\n",
"\n",
- "The participants were asked to complete the BNT voluntarily and anonymously.\n",
+ "The participants were asked to complete the graph literacy scale voluntarily and anonymously.\n",
"\n",
- "### Ethical approval / Ethikvotum\n",
+ "### Ethical approval\n",
"\n",
"All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
"\n",
- "Alle Teilnehmer mussten sich mündlich zur Teilnahme bereit erklären. Zusätzlich gaben sie vor der Studie eine informierte Einwilligung, indem sie die Hintergrundinformationen lasen und sich daraufhin für die Bereitstellung ihrer Daten entschieden. Das Ethikvotum wurde von der Ethikkommission der Ärztekammer Westfalen-Lippe und der Universität Bielefeld, Medizinische Fakultät OWL (XXXX-YYY-f-S) erteilt.\n",
- "\n",
- "### Data collection / Datenerhebung\n",
+ "### Data collection\n",
"\n",
"Data collection for this study was determined à priori as follows:\n",
"\n",
"- Input …\n",
"\n",
- "Die Datenerhebung für diese Studie wurde à priori wie folgt festgelegt:\n",
- "\n",
- "- Input …\n",
- "\n",
"``` {webr-r}\n",
"#| context: setup\n",
"\n",
@@ -133,8 +118,6 @@
"\n",
"We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) \\[@hattie2023visible\\] in outcome level between intervention and control groups (calculated *a priori* with G\\*Power 3.1) \\[@faul2007g\\]. Statistical analysis, tables and figures were conducted using R \\[@R-base\\] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages \\[@tidyverse; @gt; @patil2021visualizations\\]. Descriptive means and standard deviations were calculated for participants’ age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. …\n",
"\n",
- "Wir verwendeten das Standard-Alpha-Niveau von .05 für die Signifikanz und ein Power-Niveau von .80. Daher benötigten wir eine Stichprobengröße von mindestens XX Teilnehmern, um eine Effektgröße nachzuweisen, die einen minimal bedeutsamen Unterschied (d = .YY) \\[@hattie2023visible\\] im Ergebnisniveau zwischen Interventions- und Kontrollgruppe zeigt (*a priori* berechnet mit G\\*Power 3.1) \\[@faul2007g\\]. Statistische Analysen, Tabellen und Abbildungen wurden mit R \\[@R-base\\] in RStudio IDE (Posit Software, Boston, MA) mit den tidyverse-, gt- und ggstatsplot-Paketen \\[@tidyverse; @gt; @patil2021visualizations\\] durchgeführt. Deskriptive Mittelwerte und Standardabweichungen wurden für das Alter der Teilnehmer berechnet, und die Gesamttestwerte und Häufigkeiten wurden für das Geschlecht und für die Lösung der Fallszenarien berechnet. Die Mittelwerte und Häufigkeiten der Stichprobe wurden mit den Mittelwerten und Häufigkeiten der Grundgesamtheit unter Verwendung von t-Tests bzw. Chi-Quadrat-Tests für eine Stichprobe verglichen. …\n",
- "\n",
"``` {webr-r}\n",
"#| context: interactive\n",
"\n",
@@ -168,11 +151,9 @@
"\n",
"### Recruitment Process and Demographic Characteristics / Studienteilnahme\n",
"\n",
- "The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …\n",
- "\n",
- "Der Rekrutierungsprozess ist in Abbildung 1 dargestellt. Wir erhielten XX vollständige Datensätze (Rücklaufquote YY.Z%), nachdem wir Kontakt mit …"
+ "The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …"
],
- "id": "6a22421d-b29a-4e9c-bd35-f929e80fb65c"
+ "id": "b127b4cc-9ac4-4051-94ae-357cbb110dd5"
},
{
"cell_type": "raw",
@@ -182,7 +163,7 @@
"source": [
"<!-- Man kann Code-Ergebnisse über einfügen -->"
],
- "id": "912921e5-787e-4e2b-85b4-a85be26059e0"
+ "id": "413a72b7-1644-470f-ba9c-bf81f3b64da9"
},
{
"cell_type": "markdown",
@@ -200,7 +181,7 @@
"image/jpeg": "/9j/4AAQSkZJRgABAQAASABIAAD/4QBARXhpZgAATU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAA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\niigAooooAKKKKACiiigAooooAKKKKAP/2Q==\n"
}
},
- "id": "500c353b-a031-4b92-a624-c03b9f78731d"
+ "id": "6a27a386-4d94-4c46-a31c-23ee4ff5d7ba"
},
{
"cell_type": "raw",
@@ -210,7 +191,7 @@
"source": [
"<!-- Man kann Code-Ergebnisse über einfügen -->"
],
- "id": "de7c7c03-4a73-49a6-bda3-375d275142ae"
+ "id": "ae536c67-7cc4-47a9-8bc7-873ac58bed2c"
},
{
"cell_type": "markdown",
@@ -222,29 +203,27 @@
"\n",
"After the evaluation of all datasets, the following findings emerged. The first is that …\n",
"\n",
- "Nach Auswertung aller Datensätze ergaben sich die folgenden Erkenntnisse: Die erste ist, dass …\n",
+ "### Limitation: study population\n",
"\n",
- "### Limitation: Studienpopulation\n",
+ "### Limitation: study ndesign\n",
"\n",
- "möglicher Einfluss der Studienpopulation auf Interpretation und Anwendbarkeit der Ergebnisse …\n",
+ "### Integration with prior work\n",
"\n",
- "### Limitation: Studiendesign\n",
- "\n",
- "möglicher Einfluss des Studiendesigns auf Interpretation und Anwendbarkeit der Ergebnisse …\n",
+ "…\n",
"\n",
- "### Integration with prior work / Vergleich mit bestehender theoretischer und empirischer Forschung\n",
+ "Only a few studies provide insights into the graphical and numerical skills among medical students.\n",
"\n",
- "… is a high effect size in comparison to Hattie et al. \\[@hattie2023visible\\].\n",
+ "In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales’ mean scores were high among the medical students in this sample \\[@mas2018graphical\\].\n",
"\n",
- "### Implications for practice / Direkte Auswirkungen der Ergebnisse auf Praxis\n",
+ "### Implications for practice\n",
"\n",
"…\n",
"\n",
- "### Implications for research / Direkte Auswirkungen der Ergebnisse auf Forschung\n",
+ "### Implications for research\n",
"\n",
"…\n",
"\n",
- "## Conclusions / Schlussfolgerungen\n",
+ "## Conclusions\n",
"\n",
"…\n",
"\n",
@@ -252,53 +231,39 @@
"\n",
"## Declarations\n",
"\n",
- "### Ethics approval and consent to participate / Ethikvotum\n",
+ "### Ethics approval and consent to participate\n",
"\n",
"Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
"\n",
- "Die Teilnehmer wurden gebeten, den Test freiwillig und anonym auszufüllen. Um ein Höchstmaß an Transparenz zu erreichen, mussten alle Teilnehmer ihre mündliche Zustimmung zur Teilnahme geben. Darüber hinaus gaben sie vor der Studie ihre informierte Zustimmung, indem sie die Hintergrundinformationen lasen und sich für die Bereitstellung ihrer Daten entschieden. Eine zusätzliche schriftliche Einwilligung wurde nicht eingeholt. Die Studie und die Verwendung der ausschließlich mündlichen Einwilligung wurde von der Ethikkommission der Ärztekammer Westfalen-Lippe und der Medizinischen Fakultät OWL der Universität Bielefeld genehmigt (XXXX-YYY-f-S).\n",
- "\n",
- "### Consent for publication / Einwilligung zur Veröffentlichung\n",
+ "### Consent for publication\n",
"\n",
"Not applicable\n",
"\n",
- "Nicht zutreffend\n",
- "\n",
- "### Availability of data and materials / Verfügbarkeit von Daten und Materialien\n",
+ "### Availability of data and materials\n",
"\n",
"The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n",
"\n",
- "Die Originaldaten der Studie sind beim Open Science Framework (osf.io, siehe Manuskript-URL) verfügbar.\n",
- "\n",
"### Competing interests / Konkurrierende Interessen\n",
"\n",
"The authors declare that they have no competing interests.\n",
"\n",
- "Die Autoren erklären, dass sie keine konkurrierenden Interessen haben.\n",
- "\n",
"### Funding / Finanzierung\n",
"\n",
"The author(s) received no specific funding for this work.\n",
"\n",
- "Der/die Autor\\*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\n",
- "\n",
"### Authors’ contributions / Beiträge der Autor\\*innen\n",
"\n",
"HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.\n",
"\n",
- "HF konzipierte die Studie und beteiligte sich an deren Gestaltung und Koordination. XX war an der Datenerfassung und Datenanalyse beteiligt. YY war an der Gestaltung der Studie beteiligt. ZZ beteiligte sich an der Konzeption und Koordination der Studie. Alle Autor\\*innen haben an der Erstellung des Manuskripts mitgewirkt.\n",
- "\n",
"### CRediT authorship contribution statement\n",
"\n",
- "**Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Wolf Jonas Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Maren März:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n",
+ "**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n",
"\n",
"### Acknowledgments / Danksagung\n",
"\n",
- "The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n",
- "\n",
- "Die Autoren sind dankbar für die aufschlussreichen Kommentare der anonymen Peer-Reviewer von Medical Education Online. Die Großzügigkeit und das Fachwissen eines jeden Einzelnen haben diese Studie auf unzählige Arten verbessert und uns vor vielen Fehlern bewahrt; die, die unvermeidlich bleiben, liegen vollständig in unserer eigenen Verantwortung."
+ "The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility."
],
- "id": "706b8c76-c363-4e76-a21a-62eaea36e5c0"
+ "id": "1c423e7f-827e-42b6-9bf2-442388ef2a17"
}
],
"nbformat": 4,
diff --git a/public/index.html b/public/index.html
index c47769d..8511d2b 100644
--- a/public/index.html
+++ b/public/index.html
@@ -310,11 +310,11 @@ div.csl-indent {
<h2 id="toc-title">Inhaltsverzeichnis</h2>
<ul>
- <li><a href="#background-hintergrund" id="toc-background-hintergrund" class="nav-link active" data-scroll-target="#background-hintergrund">Background / Hintergrund</a>
+ <li><a href="#background" id="toc-background" class="nav-link active" data-scroll-target="#background">Background</a>
<ul class="collapse">
- <li><a href="#broad-problem-allgemeineres-problem" id="toc-broad-problem-allgemeineres-problem" class="nav-link" data-scroll-target="#broad-problem-allgemeineres-problem">Broad problem / Allgemeineres Problem</a></li>
- <li><a href="#theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems" id="toc-theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems" class="nav-link" data-scroll-target="#theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems">Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems</a></li>
- <li><a href="#focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt" id="toc-focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt" class="nav-link" data-scroll-target="#focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt">Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt</a></li>
+ <li><a href="#broad-problem" id="toc-broad-problem" class="nav-link" data-scroll-target="#broad-problem">Broad problem</a></li>
+ <li><a href="#theoretical-andor-empirical-focus-of-the-problem" id="toc-theoretical-andor-empirical-focus-of-the-problem" class="nav-link" data-scroll-target="#theoretical-andor-empirical-focus-of-the-problem">Theoretical and/or empirical focus of the problem</a></li>
+ <li><a href="#focused-problem-statement" id="toc-focused-problem-statement" class="nav-link" data-scroll-target="#focused-problem-statement">Focused problem statement</a></li>
<li><a href="#statement-of-study-intent" id="toc-statement-of-study-intent" class="nav-link" data-scroll-target="#statement-of-study-intent">Statement of study intent</a></li>
</ul></li>
<li><a href="#methods" id="toc-methods" class="nav-link" data-scroll-target="#methods">Methods</a>
@@ -331,16 +331,16 @@ div.csl-indent {
<li><a href="#recruitment-process-and-demographic-characteristics-studienteilnahme" id="toc-recruitment-process-and-demographic-characteristics-studienteilnahme" class="nav-link" data-scroll-target="#recruitment-process-and-demographic-characteristics-studienteilnahme">Recruitment Process and Demographic Characteristics / Studienteilnahme</a></li>
<li><a href="#primary-and-secondary-outcomes-haupt--und-nebenergebnisse" id="toc-primary-and-secondary-outcomes-haupt--und-nebenergebnisse" class="nav-link" data-scroll-target="#primary-and-secondary-outcomes-haupt--und-nebenergebnisse">Primary and secondary Outcomes / Haupt- und Nebenergebnisse</a></li>
</ul></li>
- <li><a href="#discussion-diskussion" id="toc-discussion-diskussion" class="nav-link" data-scroll-target="#discussion-diskussion">Discussion / Diskussion</a>
+ <li><a href="#discussion" id="toc-discussion" class="nav-link" data-scroll-target="#discussion">Discussion</a>
<ul class="collapse">
<li><a href="#summary-zusammenfassung-der-ergebnisse" id="toc-summary-zusammenfassung-der-ergebnisse" class="nav-link" data-scroll-target="#summary-zusammenfassung-der-ergebnisse">Summary / Zusammenfassung der Ergebnisse</a></li>
<li><a href="#limitation-study-population" id="toc-limitation-study-population" class="nav-link" data-scroll-target="#limitation-study-population">Limitation: study population</a></li>
- <li><a href="#limitation-study-ndesign" id="toc-limitation-study-ndesign" class="nav-link" data-scroll-target="#limitation-study-ndesign">Limitation: study ndesign</a></li>
+ <li><a href="#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>
- </ul></li>
<li><a href="#conclusions" id="toc-conclusions" class="nav-link" data-scroll-target="#conclusions">Conclusions</a></li>
+ </ul></li>
<li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
</ul>
@@ -393,7 +393,7 @@ STRUKTUR DES MANUSKRIPTS
</div>
<div id="callout-2" class="callout-2-contents callout-collapse collapse">
<div class="callout-body-container callout-body">
-<p><span style="color: grey;"><strong>Relevantes (Studierenden-)Problem:</strong> <!-- Allgemeineres Problem: mit einem „Helden“ (Studierende oder v.a. junge Ärzte), man selbst als Wegbereiter, Trainer, „Enabler“ --> Die Akzeptanz von dem XYZ-THEMA hat deutlich zugenommen. Für einen Einsatz im professionellen Bereich sind die Leistungen bisher aber allenfalls ausreichend.<br>
+<p><span style="color: grey;"><strong>Relevantes Problem:</strong> Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant.<br>
<strong>Fokussiertes Problem:</strong> Studienlage zu THEMA allgemein und Medical-Education-Kontext;<br>
Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus.<br>
<strong>Gap des Problems:</strong> <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend.<br>
@@ -409,22 +409,22 @@ Wie sieht die Leistung bei detaillierter Betrachtung der Domänen und Kompetenzl
</div>
</div>
</div>
-<section id="background-hintergrund" class="level2 page-columns page-full">
-<h2 class="anchored" data-anchor-id="background-hintergrund">Background / Hintergrund</h2>
-<section id="broad-problem-allgemeineres-problem" class="level3">
-<h3 class="anchored" data-anchor-id="broad-problem-allgemeineres-problem">Broad problem / Allgemeineres Problem</h3>
+<section id="background" class="level2 page-columns page-full">
+<h2 class="anchored" data-anchor-id="background">Background</h2>
+<section id="broad-problem" class="level3">
+<h3 class="anchored" data-anchor-id="broad-problem">Broad problem</h3>
<p>Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.</p>
</section>
-<section id="theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems" class="level3 page-columns page-full">
-<h3 class="anchored" data-anchor-id="theoretical-andor-empirical-focus-of-the-problem-theoretische-undoder-empirische-fokussierung-des-problems">Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems</h3>
+<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>Processing those visual representations is essential for understanding scientific and statistical data <span class="citation" data-cites="friel2001making">[<a href="#ref-friel2001making" role="doc-biblioref">1</a>]</span> and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).</p>
<div class="no-row-height 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.
</div></div><p>In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.</p>
</section>
-<section id="focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt" class="level3">
-<h3 class="anchored" data-anchor-id="focused-problem-statement-fokussiertes-problem-statement-gap-und-möglicher-fortschritt">Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt</h3>
+<section id="focused-problem-statement" class="level3">
+<h3 class="anchored" data-anchor-id="focused-problem-statement">Focused problem statement</h3>
<p>Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.</p>
<p>When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.</p>
</section>
@@ -692,24 +692,26 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
<h3 class="anchored" data-anchor-id="primary-and-secondary-outcomes-haupt--und-nebenergebnisse">Primary and secondary Outcomes / Haupt- und Nebenergebnisse</h3>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
-<p><a href="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="lightbox" data-gallery="quarto-lightbox-gallery-1" data-glightbox="description: .lightbox-desc-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>
+<p><a href="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="lightbox" data-glightbox="description: .lightbox-desc-1" data-gallery="quarto-lightbox-gallery-1" title="Beispielgrafik: ein Bild sagt mehr als tausend Worte …"><img src="Durchschnittswerte_Selbsteinschätzung_NKLM_14a.jpg" class="img-fluid figure-img" alt="Beispielgrafik: ein Bild sagt mehr als tausend Worte …"></a></p>
<figcaption>Beispielgrafik: ein Bild sagt mehr als tausend Worte …</figcaption>
</figure>
</div>
<!-- Man kann Code-Ergebnisse über einfügen -->
</section>
</section>
-<section id="discussion-diskussion" class="level2 page-columns page-full">
-<h2 class="anchored" data-anchor-id="discussion-diskussion">Discussion / Diskussion</h2>
+<section id="discussion" class="level2 page-columns page-full">
+<h2 class="anchored" data-anchor-id="discussion">Discussion</h2>
<section id="summary-zusammenfassung-der-ergebnisse" class="level3">
<h3 class="anchored" data-anchor-id="summary-zusammenfassung-der-ergebnisse">Summary / Zusammenfassung der Ergebnisse</h3>
<p>After the evaluation of all datasets, the following findings emerged. The first is that …</p>
</section>
<section id="limitation-study-population" class="level3">
<h3 class="anchored" data-anchor-id="limitation-study-population">Limitation: study population</h3>
+<p>…</p>
</section>
-<section id="limitation-study-ndesign" class="level3">
-<h3 class="anchored" data-anchor-id="limitation-study-ndesign">Limitation: study ndesign</h3>
+<section id="limitation-study-design" class="level3">
+<h3 class="anchored" data-anchor-id="limitation-study-design">Limitation: study design</h3>
+<p>…</p>
</section>
<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>
@@ -727,11 +729,11 @@ ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth
<h3 class="anchored" data-anchor-id="implications-for-research">Implications for research</h3>
<p>…</p>
</section>
-</section>
-<section id="conclusions" class="level2">
-<h2 class="anchored" data-anchor-id="conclusions">Conclusions</h2>
+<section id="conclusions" class="level3">
+<h3 class="anchored" data-anchor-id="conclusions">Conclusions</h3>
<p>…</p>
</section>
+</section>
<section id="references" class="level2 unnumbered">
<h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
@@ -1320,7 +1322,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
});
</script>
</div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","selector":".lightbox","loop":false,"openEffect":"zoom","descPosition":"bottom"});
+<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","loop":false,"selector":".lightbox","descPosition":"bottom","openEffect":"zoom"});
window.onload = () => {
lightboxQuarto.on('slide_before_load', (data) => {
const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/public/index.out.ipynb b/public/index.out.ipynb
index dd00cca..8e38a62 100644
--- a/public/index.out.ipynb
+++ b/public/index.out.ipynb
@@ -34,7 +34,7 @@
"\n",
"> **STRUKTUR DES MANUSKRIPTS**\n",
">\n",
- "> <span color=\"grey\">**Relevantes (Studierenden-)Problem:** <!-- Allgemeineres Problem: mit einem „Helden“ (Studierende oder v.a. junge Ärzte), man selbst als Wegbereiter, Trainer, „Enabler“ --> Die Akzeptanz von dem XYZ-THEMA hat deutlich zugenommen. Für einen Einsatz im professionellen Bereich sind die Leistungen bisher aber allenfalls ausreichend. \n",
+ "> <span color=\"grey\">**Relevantes Problem:** Graph Literacy ist wichtig im Rahmen der Health Literacy. Damit ist sie auch für die Ausbildung der Studierenden relevant. \n",
"> **Fokussiertes Problem:** Studienlage zu THEMA allgemein und Medical-Education-Kontext; \n",
"> Progress Tests eignen sich besonders zur Messung von Fortschritt durch Vergleich mit verschiedenen Ausbildungsniveaus. \n",
"> **Gap des Problems:** <!-- Gap / Dilemma / Widerspruch subj. Erwartung und Realität --> Es gibt eine hohe Erwartung an den Einsatz von THEMA in der Medizin. Die bisherigen Leistungen sind auch in der Medizin bisher aber allenfalls ausreichend. \n",
@@ -48,13 +48,13 @@
"> **Ergebnisparameter:** Anzahl der richtigen Antworten insgesamt und pro Domäne bzw. Kompetenzlevel \n",
"> **Statistik:** Bestimmung der Prozentwerte für die absolute und z-Scores und Percentilen für die relative Bewertung der Leistungen.</span>\n",
"\n",
- "## Background / Hintergrund\n",
+ "## Background\n",
"\n",
- "### Broad problem / Allgemeineres Problem\n",
+ "### Broad problem\n",
"\n",
"Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.\n",
"\n",
- "### Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems\n",
+ "### Theoretical and/or empirical focus of the problem\n",
"\n",
"One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.\n",
"\n",
@@ -62,56 +62,41 @@
"\n",
"In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.\n",
"\n",
- "### Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt\n",
+ "### Focused problem statement\n",
"\n",
"Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.\n",
"\n",
"When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.\n",
"\n",
- "### Statement of study intent / Fokussierte Forschungsfrage/n\n",
+ "### Statement of study intent\n",
"\n",
"We performed a study of medical students to investigate the following questions:\n",
"\n",
"1. What is …\n",
"2. Why are …\n",
"\n",
- "Wir haben eine Studie mit Medizinstudierenden durchgeführt, um die folgenden Fragen zu untersuchen:\n",
+ "## Methods\n",
"\n",
- "1. Was ist …\n",
- "2. Warum sind …\n",
+ "### Setting and subjects\n",
"\n",
- "## Methods / Methoden\n",
- "\n",
- "### Setting and subjects / Setting und Probanden\n",
- "\n",
- "Our study was conducted at Medical School …\n",
- "\n",
- "Unsere Studie wurde an der Medizinischen Fakultät der … durchgeführt.\n",
+ "Our study was conducted at Medical Faculty of Münster …\n",
"\n",
"It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students’ clinical experience, they are rotated in various hospital departments during their final year (“clinical/practical†year). …\n",
"\n",
- "Das Medizinstudium in Deutschland dauert sechs Jahre, wobei die Studierenden direkt von den weiterführenden Schulen aufgenommen werden. Das Studium gliedert sich in einen vorklinischen Teil (die ersten beiden Jahre) und einen klinischen Teil (die letzten vier Jahre). Um die klinische Erfahrung der Studenten zu verbessern, werden sie während ihres letzten Jahres (klinisch-praktisches Jahr) in verschiedenen Krankenhausabteilungen eingesetzt.\n",
- "\n",
"### Study design / Studiendesign\n",
"\n",
- "The participants were asked to complete the BNT voluntarily and anonymously.\n",
+ "The participants were asked to complete the graph literacy scale voluntarily and anonymously.\n",
"\n",
- "### Ethical approval / Ethikvotum\n",
+ "### Ethical approval\n",
"\n",
"All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
"\n",
- "Alle Teilnehmer mussten sich mündlich zur Teilnahme bereit erklären. Zusätzlich gaben sie vor der Studie eine informierte Einwilligung, indem sie die Hintergrundinformationen lasen und sich daraufhin für die Bereitstellung ihrer Daten entschieden. Das Ethikvotum wurde von der Ethikkommission der Ärztekammer Westfalen-Lippe und der Universität Bielefeld, Medizinische Fakultät OWL (XXXX-YYY-f-S) erteilt.\n",
- "\n",
- "### Data collection / Datenerhebung\n",
+ "### Data collection\n",
"\n",
"Data collection for this study was determined à priori as follows:\n",
"\n",
"- Input …\n",
"\n",
- "Die Datenerhebung für diese Studie wurde à priori wie folgt festgelegt:\n",
- "\n",
- "- Input …\n",
- "\n",
"``` {webr-r}\n",
"#| context: setup\n",
"\n",
@@ -133,8 +118,6 @@
"\n",
"We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) \\[[2](#ref-hattie2023visible)\\] in outcome level between intervention and control groups (calculated *a priori* with G\\*Power 3.1) \\[[3](#ref-faul2007g)\\]. Statistical analysis, tables and figures were conducted using R \\[[4](#ref-R-base)\\] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages \\[[5](#ref-tidyverse)–[7](#ref-patil2021visualizations)\\]. Descriptive means and standard deviations were calculated for participants’ age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. …\n",
"\n",
- "Wir verwendeten das Standard-Alpha-Niveau von .05 für die Signifikanz und ein Power-Niveau von .80. Daher benötigten wir eine Stichprobengröße von mindestens XX Teilnehmern, um eine Effektgröße nachzuweisen, die einen minimal bedeutsamen Unterschied (d = .YY) \\[[2](#ref-hattie2023visible)\\] im Ergebnisniveau zwischen Interventions- und Kontrollgruppe zeigt (*a priori* berechnet mit G\\*Power 3.1) \\[[3](#ref-faul2007g)\\]. Statistische Analysen, Tabellen und Abbildungen wurden mit R \\[[4](#ref-R-base)\\] in RStudio IDE (Posit Software, Boston, MA) mit den tidyverse-, gt- und ggstatsplot-Paketen \\[[5](#ref-tidyverse)–[7](#ref-patil2021visualizations)\\] durchgeführt. Deskriptive Mittelwerte und Standardabweichungen wurden für das Alter der Teilnehmer berechnet, und die Gesamttestwerte und Häufigkeiten wurden für das Geschlecht und für die Lösung der Fallszenarien berechnet. Die Mittelwerte und Häufigkeiten der Stichprobe wurden mit den Mittelwerten und Häufigkeiten der Grundgesamtheit unter Verwendung von t-Tests bzw. Chi-Quadrat-Tests für eine Stichprobe verglichen. …\n",
- "\n",
"``` {webr-r}\n",
"#| context: interactive\n",
"\n",
@@ -168,11 +151,9 @@
"\n",
"### Recruitment Process and Demographic Characteristics / Studienteilnahme\n",
"\n",
- "The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …\n",
- "\n",
- "Der Rekrutierungsprozess ist in Abbildung 1 dargestellt. Wir erhielten XX vollständige Datensätze (Rücklaufquote YY.Z%), nachdem wir Kontakt mit …"
+ "The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting …"
],
- "id": "c90fe026-087d-4765-8517-3fcea1fb25ad"
+ "id": "7a82bf6f-0c41-4b7d-bd9c-598d635b0e75"
},
{
"cell_type": "raw",
@@ -182,7 +163,7 @@
"source": [
"<!-- Man kann Code-Ergebnisse über einfügen -->"
],
- "id": "f68f3f27-3e4c-4edb-87b1-996030152c8a"
+ "id": "5696d52b-f232-45ba-b55c-bbd8229fc1f8"
},
{
"cell_type": "markdown",
@@ -200,7 +181,7 @@
"image/jpeg": "/9j/4AAQSkZJRgABAQAASABIAAD/4QBARXhpZgAATU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAA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\niigAooooAKKKKACiiigAooooAKKKKAP/2Q==\n"
}
},
- "id": "2d060302-8787-45cc-8f8c-b3dcd823d243"
+ "id": "7420951e-bf1e-4e99-b2bc-0468f0d893c8"
},
{
"cell_type": "raw",
@@ -210,7 +191,7 @@
"source": [
"<!-- Man kann Code-Ergebnisse über einfügen -->"
],
- "id": "a72cd038-d8a7-4569-8ecc-69e4d305ff14"
+ "id": "5d0421b7-6839-44d7-b0e2-a0f4b535f4ce"
},
{
"cell_type": "markdown",
@@ -222,29 +203,27 @@
"\n",
"After the evaluation of all datasets, the following findings emerged. The first is that …\n",
"\n",
- "Nach Auswertung aller Datensätze ergaben sich die folgenden Erkenntnisse: Die erste ist, dass …\n",
+ "### Limitation: study population\n",
"\n",
- "### Limitation: Studienpopulation\n",
+ "### Limitation: study ndesign\n",
"\n",
- "möglicher Einfluss der Studienpopulation auf Interpretation und Anwendbarkeit der Ergebnisse …\n",
+ "### Integration with prior work\n",
"\n",
- "### Limitation: Studiendesign\n",
- "\n",
- "möglicher Einfluss des Studiendesigns auf Interpretation und Anwendbarkeit der Ergebnisse …\n",
+ "…\n",
"\n",
- "### Integration with prior work / Vergleich mit bestehender theoretischer und empirischer Forschung\n",
+ "Only a few studies provide insights into the graphical and numerical skills among medical students.\n",
"\n",
- "… is a high effect size in comparison to Hattie et al. \\[[2](#ref-hattie2023visible)\\].\n",
+ "In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales’ mean scores were high among the medical students in this sample \\[[8](#ref-mas2018graphical)\\].\n",
"\n",
- "### Implications for practice / Direkte Auswirkungen der Ergebnisse auf Praxis\n",
+ "### Implications for practice\n",
"\n",
"…\n",
"\n",
- "### Implications for research / Direkte Auswirkungen der Ergebnisse auf Forschung\n",
+ "### Implications for research\n",
"\n",
"…\n",
"\n",
- "## Conclusions / Schlussfolgerungen\n",
+ "## Conclusions\n",
"\n",
"…\n",
"\n",
@@ -264,55 +243,43 @@
"\n",
"7\\. Patil I. Visualizations with statistical details: The’ggstatsplot’approach. Journal of Open Source Software. 2021;6:3167.\n",
"\n",
+ "8\\. Mas G, Tello T, Ortiz P, Petrova D, GarcıÌa-Retamero R. Graphical and numerical skills in pre-and postgraduate medical students from a private university. Gac Med Mex. 2018;154:163–9.\n",
+ "\n",
"## Declarations\n",
"\n",
- "### Ethics approval and consent to participate / Ethikvotum\n",
+ "### Ethics approval and consent to participate\n",
"\n",
"Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).\n",
"\n",
- "Die Teilnehmer wurden gebeten, den Test freiwillig und anonym auszufüllen. Um ein Höchstmaß an Transparenz zu erreichen, mussten alle Teilnehmer ihre mündliche Zustimmung zur Teilnahme geben. Darüber hinaus gaben sie vor der Studie ihre informierte Zustimmung, indem sie die Hintergrundinformationen lasen und sich für die Bereitstellung ihrer Daten entschieden. Eine zusätzliche schriftliche Einwilligung wurde nicht eingeholt. Die Studie und die Verwendung der ausschließlich mündlichen Einwilligung wurde von der Ethikkommission der Ärztekammer Westfalen-Lippe und der Medizinischen Fakultät OWL der Universität Bielefeld genehmigt (XXXX-YYY-f-S).\n",
- "\n",
- "### Consent for publication / Einwilligung zur Veröffentlichung\n",
+ "### Consent for publication\n",
"\n",
"Not applicable\n",
"\n",
- "Nicht zutreffend\n",
- "\n",
- "### Availability of data and materials / Verfügbarkeit von Daten und Materialien\n",
+ "### Availability of data and materials\n",
"\n",
"The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).\n",
"\n",
- "Die Originaldaten der Studie sind beim Open Science Framework (osf.io, siehe Manuskript-URL) verfügbar.\n",
- "\n",
"### Competing interests / Konkurrierende Interessen\n",
"\n",
"The authors declare that they have no competing interests.\n",
"\n",
- "Die Autoren erklären, dass sie keine konkurrierenden Interessen haben.\n",
- "\n",
"### Funding / Finanzierung\n",
"\n",
"The author(s) received no specific funding for this work.\n",
"\n",
- "Der/die Autor\\*innen erhielt(en) für diese Arbeit keine spezielle Finanzierung.\n",
- "\n",
"### Authors’ contributions / Beiträge der Autor\\*innen\n",
"\n",
"HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.\n",
"\n",
- "HF konzipierte die Studie und beteiligte sich an deren Gestaltung und Koordination. XX war an der Datenerfassung und Datenanalyse beteiligt. YY war an der Gestaltung der Studie beteiligt. ZZ beteiligte sich an der Konzeption und Koordination der Studie. Alle Autor\\*innen haben an der Erstellung des Manuskripts mitgewirkt.\n",
- "\n",
"### CRediT authorship contribution statement\n",
"\n",
- "**Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Wolf Jonas Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Maren März:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n",
+ "**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.\n",
"\n",
"### Acknowledgments / Danksagung\n",
"\n",
- "The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.\n",
- "\n",
- "Die Autoren sind dankbar für die aufschlussreichen Kommentare der anonymen Peer-Reviewer von Medical Education Online. Die Großzügigkeit und das Fachwissen eines jeden Einzelnen haben diese Studie auf unzählige Arten verbessert und uns vor vielen Fehlern bewahrt; die, die unvermeidlich bleiben, liegen vollständig in unserer eigenen Verantwortung."
+ "The authors are grateful for the insightful comments offered by the anonymous peer reviewers at Medical Education Online. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility."
],
- "id": "1fdc4ed6-1f1c-43b7-b92f-4e585f21aeda"
+ "id": "1e1748df-86d6-44cf-bb9f-89bc237b5b1d"
}
],
"nbformat": 4,
diff --git a/public/index.qmd b/public/index.qmd
index 87b47ec..b7a54db 100644
--- a/public/index.qmd
+++ b/public/index.qmd
@@ -64,32 +64,7 @@ This manuscript is a work in progress. However, thank you for your interest. Ple
[{{< meta plain-language-summary >}}]{color="grey"}
:::
-## Background / Hintergrund
-
-### Broad problem / Allgemeineres Problem
-
-Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.
-
-### Theoretical and/or empirical focus of the problem / Theoretische und/oder empirische Fokussierung des Problems
-
-One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.
-
-Processing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).
-
-In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.
-
-### Focused problem statement / Fokussiertes Problem-Statement: Gap und möglicher Fortschritt
-
-Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.
-
-When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.
-
-### Statement of study intent
-
-We performed a study of medical students to investigate the following questions:
-
-1. What is ...
-2. Why are ...
+{{< include _background.md >}}
## Methods
diff --git a/text/_abstract.md b/text/_abstract.md
new file mode 100644
index 0000000..91e84b2
--- /dev/null
+++ b/text/_abstract.md
@@ -0,0 +1,8 @@
+
+**Background / Hintergrund**: ...
+
+**Methods / Methoden**: ...
+
+**Results / Ergebnisse**: ...
+
+**Conclusio / Schlussfolgerungen**: ...
\ No newline at end of file
diff --git a/text/_background.md b/text/_background.md
new file mode 100644
index 0000000..7c85a27
--- /dev/null
+++ b/text/_background.md
@@ -0,0 +1,25 @@
+
+### Broad problem
+
+Health literacy depends on diverse aspects of skills in processing information. To adequately understand medical reports, treatments and study results a set of abilities is needed. Thinking of future physicians one can imagine a multitude of situations where a high health literacy is required: whenever talking with patients about medical data, consenting in treatments and educating patients about diseases, making clinical decisions depending on laboratory results, imaging and study results, understanding evidence, interpretation of epidemiological data and communication in medical teams.
+
+### Theoretical and/or empirical focus of the problem
+
+One important aspect of health literacy is graph literacy, meaning the reading and understanding of graphs. This process is depending on decoding and interpreting signs and symbols and known as semiotic activity. Thus, the ability to understand graphs should not be considered isolatioted from other forms of literacy. It is an integral part of the ability to process and communicate information effectively in a world that is increasingly dependent on data and its visual representation.
+
+Processing those visual representations is essential for understanding scientific and statistical data [@friel2001making] and particularly relevant in areas such as medical research where graphs and data visualizations are frequently used to convey complex information. A personal understanding of the representations is essential when preparing data for communication in order to ensure adequate knowledge transfer to others (Cooper et al, 2002). But misleading representations (either through deliberate manipulation or unintentionally through errors or incompleteness) can also have a significant influence on the reception of information by the recipient (Melnik-Leroy, 2023).
+
+In summary it ca be said that graph literacy, as a form of semiotic activity, is a crucial component of overall literacy (Roth, 2002). It can have an impact on risk comprehension (Okan, 2013), suggesting that a higher graph literacy may be associated with a better decision-making performance. However, studies of graph literacy mainly refer to patients (Durand et al, 2020) or the ability of doctors (Caverly et al, 2015) to interpret graphical representations.
+
+### Focused problem statement
+
+Especially for those advising and informing people with less health and graph literacy, it is important to achieve high competence in graph literacy themselves. But lack of understanding of visual representations can significantly impact decision making for patients and for (future) medical doctors.
+
+When providing information to patients, medical doctors must be aware about patient health literacy and about their own. Therefore, we conducted a cohort study with medical students for understanding their ability to interpret medical information provided visually.
+
+### Statement of study intent
+
+We performed a study of medical students to investigate the following questions:
+
+1. What is ...
+2. Why are ...
diff --git a/text/_declarations.md b/text/_declarations.md
new file mode 100644
index 0000000..105f1c2
--- /dev/null
+++ b/text/_declarations.md
@@ -0,0 +1,33 @@
+
+
+### Ethics approval and consent to participate
+
+Participants were asked to complete the test voluntarily and anonymously. To achieve maximum transparency, all participants had to verbally agree to participate. Additionally, they provided their informed consent prior to the study by reading the background information and choosing to provide data. An extra written consent was not obtained. The study and the use of only verbal consent was approved by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).
+
+### Consent for publication
+
+Not applicable
+
+### Availability of data and materials
+
+The original data that support the findings of this study are available from Open Science Framework (osf.io, see manuscript-URL).
+
+### Competing interests / Konkurrierende Interessen
+
+The authors declare that they have no competing interests.
+
+### Funding / Finanzierung
+
+The author(s) received no specific funding for this work.
+
+### Authors' contributions / Beiträge der Autor\*innen
+
+HF conceived the study and participated in its design and coordination. XX participated in the data acquisition and data analysis. YY participated in the study design. ZZ participated in the design and coordination of the study. All authors helped to draft the manuscript.
+
+### CRediT authorship contribution statement
+
+**Janina Soler Wenglein:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft. **Hendrik Friederichs:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing, Writing - original draft.
+
+### Acknowledgments / Danksagung
+
+The authors are grateful for the insightful comments offered by the anonymous peer reviewers at {{< meta citation.container-title >}}. The generosity and expertise of one and all have improved this study in innumerable ways and saved us from many errors; those that inevitably remain are entirely our own responsibility.
\ No newline at end of file
diff --git a/text/_discussion.md b/text/_discussion.md
new file mode 100644
index 0000000..45778fa
--- /dev/null
+++ b/text/_discussion.md
@@ -0,0 +1,31 @@
+### Summary / Zusammenfassung der Ergebnisse
+
+After the evaluation of all datasets, the following findings emerged. The first is that ...
+
+### Limitation: study population
+
+...
+
+### Limitation: study design
+
+...
+
+### Integration with prior work
+
+...
+
+Only a few studies provide insights into the graphical and numerical skills among medical students.
+
+In a cross-sectional, descriptive study, the researchers applied the Objective Numeracy, Subjective Numeracy, and Graph Literacy Scales to medical students in their final two years of medical school and to medical residents. The study included 169 participants, comprising 70% sixth-year seventh-year students, and 30% residents. The findings showed that the mean graph literacy was 10.35. A multiple linear regression analysis revealed that higher scores in the Graph Literacy Scale were associated with the male gender and younger age. The study concluded that numeracy and graph literacy scales' mean scores were high among the medical students in this sample [@mas2018graphical].
+
+### Implications for practice
+
+...
+
+### Implications for research
+
+...
+
+### Conclusions
+
+...
diff --git a/text/_methods.md b/text/_methods.md
new file mode 100644
index 0000000..df22132
--- /dev/null
+++ b/text/_methods.md
@@ -0,0 +1,65 @@
+
+### Setting and subjects
+
+Our study was conducted at Medical Faculty of Münster ...
+
+It takes six years to complete a course in medical school in Germany, with students enrolled directly from secondary schools. The course of study is divided into a pre-clinical section (the first two years) and a clinical section (the last four years). To improve students' clinical experience, they are rotated in various hospital departments during their final year ("clinical/practical" year). ...
+
+### Study design / Studiendesign
+
+The participants were asked to complete the graph literacy scale voluntarily and anonymously.
+
+### Ethical approval
+
+All participants had to agree verbally to participate. Additionally, they provided informed consent prior to the study by reading the background information and choosing to provide data. Ethical approval was given by the Ethics Committee of the Chamber of Physicians at Westfalen-Lippe and Bielefeld University, Medical School OWL (XXXX-YYY-f-S).
+
+### Data collection
+
+Data collection for this study was determined à priori as follows:
+
+- Input ...
+
+```{webr-r}
+#| context: setup
+
+# Download a dataset
+download.file(
+ 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
+ 'penguins.csv'
+)
+
+# Read the data
+df_penguins = read.csv("penguins.csv")
+```
+
+### Outcome Measures / Ergebnisparameter
+
+...
+
+### Statistical methods / Statistische Methoden
+
+We used the standard alpha level of .05 for significance and a power level of .80. Therefore, we needed a sample size of at least XX participants to detect an effect size showing a minimally important difference (d = .YY) [@hattie2023visible] in outcome level between intervention and control groups (calculated *a priori* with G\*Power 3.1) [@faul2007g]. Statistical analysis, tables and figures were conducted using R [@R-base] in RStudio IDE (Posit Software, Boston, MA) with the tidyverse-, gt- and ggstatsplot-packages [@tidyverse; @gt; @patil2021visualizations]. Descriptive means and standard deviations were calculated for participants' age, and total test scores and frequencies were calculated for sex and for solving the case scenarios. Sample means and frequencies were compared with population means and frequencies using one-sample t-tests and chi-square tests, respectively. ...
+
+```{webr-r}
+#| context: interactive
+
+# Download a dataset
+download.file(
+ 'https://raw.githubusercontent.com/coatless/raw-data/main/penguins.csv',
+ 'penguins.csv'
+) # <1>
+
+# Read the data
+penguins = read.csv("penguins.csv") # <2>
+
+# Scatterplot example: penguin bill length versus bill depth
+ggplot2::ggplot(data = penguins, ggplot2::aes(x = bill_length_mm, y = bill_depth_mm)) + # <3>
+ ggplot2::geom_point(ggplot2::aes(color = species,
+ shape = species), # <3>
+ size = 2) +
+ ggplot2::scale_color_manual(values = c("darkorange","darkorchid","cyan4")) # <3>
+```
+
+1. Download the dataset
+2. Read the data
+3. Build a scatterplot
\ No newline at end of file
diff --git a/text/_results.md b/text/_results.md
new file mode 100644
index 0000000..97f16a0
--- /dev/null
+++ b/text/_results.md
@@ -0,0 +1,11 @@
+### Recruitment Process and Demographic Characteristics / Studienteilnahme
+
+The recruitment process is shown in Figure 1. We obtained XX complete data sets (return rate YY.Z%) after contacting ...
+
+<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
+
+### Primary and secondary Outcomes / Haupt- und Nebenergebnisse
+
+
+
+<!-- Man kann Code-Ergebnisse über {{< embed notebooks/EDA.qmd#fig-map >}} einfügen -->
\ No newline at end of file
--
GitLab