Medical Student Mental Health
Burnout, Empathy, Anxiety and Depression
By [source]
About this dataset
This dataset explores medical students' empathy, mental health, and burnout in Switzerland. It compiles important demographic information as well as self-reported data and results from psychological tests to give a comprehensive picture of the mental states of students in the medical field. Through this research, we hope to gain a better understanding of how being a medical student can affect health and wellbeing. By looking at the individual factors that may contribute to different outcomes, we can work towards improving educational systems for the benefit of both students and their eventual patients
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How to use the dataset
This dataset contains information on the mental health, empathy and burnout of medical students in Switzerland. It includes data about demographic factors such as age, sex and language spoken as well as internal measures such as job satisfaction, psychological distress, education grades, self-reported health status and empathy rating scales. The objective of this dataset is to gain insight into the wellbeing of medical students in order to promote better policies by understanding relationships between these variables. Thus, this dataset can be used to identify how different variables may be related to the mental health of medical students.
In order to use this dataset effectively some guidelines must be followed:
- Read through the codebook provided with each column before working with the data so you can understand it more deeply
- Create visualizations such as line charts or bar graphs that show relationships between two or more variables
- Carefully inspect each variable for any outliers that may affect your analysis
- Consider specific questions you wish to answer when working with the data and make sure your approach is targeting those questions
5a .When analyzing continuous numerical data (variables such as age or stai_t), descriptive statistics should be used alongside other analytic methods such s correlation analyses .
5b . When analyzing categorical data (sex/language spoken/job etc.), create count/percentage tables that provide an overview of all categories across a particular variable 6c If a large data set is being analysed , consider using machine learning techniques for further insights
Research Ideas
- Exploring the correlation between gender, job satisfaction and empathy in medical students
- Investigating how language spoken by medical students relates to their mental health and burnout levels
- Predicting mental health and burnout levels in medical students based on different demographic variables such as age, sex, year of study and health status
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: Data Carrard et al. 2022 MedTeach.csv
| Column name |
Description |
| age |
Age of the participant. (Integer) |
| year |
Year of study of the participant. (Integer) |
| sex |
Gender of the participant. (String) |
| glang |
Language spoken by the participant. (String) |
| job |
Job of the participant. (String) |
| stud_h |
Hours of study per week of the participant. (Integer) |
| health |
Self-reported health status of the participant. (String) |
| psyt |
Psychological distress score of the participant. (Integer) |
| jspe |
Job satisfaction score of the participant. (Integer) |
| qcae_cog |
Cognitive empathy score of the participant. (Integer) |
| qcae_aff |
Affective empathy score of the participant. (Integer) |
| amsp |
Academic motivation score of the participant. (Integer) |
| erec_mean |
Empathy rating score mean of the participant. (Integer) |
| cesd |
Center for Epidemiologic Studies Depression scale of the participant. (Integer) |
| stai_t |
State-Trait Anxiety Inventory scale of the participant. (Integer) |
| mbi_ex |
Maslach Burnout Inventory-Exhaustion scale of the participant. (Integer) |
| mbi_cy |
Maslach Burnout Inventory - Cynicism Scale of the participant. (Integer) |
| mbi_ea |
Maslach Burnout Inventory - Professional Efficacy Scale of the participant. (Integer) |
File: Codebook Carrard et al. 2022 MedTeach.csv
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .