Medical Student Stress and Wellbeing Indicators

Columns

• 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). A separate Codebook Carrard et al. 2022 MedTeach.csv file is also provided to aid in understanding the variables.

Distribution

Usage

• Identifying relationships between various variables and the mental health of medical students.
• Creating visualisations such as line charts or bar graphs to show relationships between two or more variables.
• Conducting descriptive statistics and correlation analyses for continuous numerical data like age or STAI-T scores.
• Generating count/percentage tables for categorical data such as sex, language spoken, or job type.
• Applying machine learning techniques for deeper insights, especially with larger datasets.

• Exploring the correlation between gender, job satisfaction, and empathy in medical students.
• Investigating how the language spoken by medical students relates to their mental health and burnout levels.
• Predicting mental health and burnout levels based on demographic variables like age, sex, year of study, and health status.

Coverage

• Geographic Scope: Switzerland.
• Demographic Scope: Specifically focuses on medical students.
• Data Content: It includes demographic factors (age, sex, language spoken), internal measures (job satisfaction, psychological distress, education grades, self-reported health status), and empathy rating scales. The data is compiled from self-reported information and results from psychological tests.

License

Who Can Use It

• Researchers and academics studying mental health, wellbeing, and educational outcomes in healthcare fields.
• Public health professionals and policy makers looking to understand and improve the support systems for medical students.
• Data scientists and analysts interested in applying statistical and machine learning methods to health survey data.
• Anyone interested in the psychological aspects and challenges faced by students in demanding academic programmes.

Dataset Name Suggestions

• Medical Student Wellbeing and Mental Health in Switzerland
• Swiss Medical Student Empathy, Burnout, and Mental Health
• Healthcare Student Psychological Data (Switzerland)
• Medical Student Stress and Wellbeing Indicators

Attributes