Cultural variations in wellbeing, burnout and substance use amongst medical students in twelve countries
Bibliographic record
Abstract
High levels of stress, burnout, and symptoms of poor mental health have been well known among practicing doctors for a number of years. Indeed, many health systems have formal and informal mechanisms to offer support and treatment where needed, though this varies tremendously across cultures. There is increasing evidence that current medical students, our doctors of the future, also report very high levels of distress, burnout, and substance misuse. We sampled large groups of medical students in 12 countries at the same time and with exactly the same method in order to aid direct comparison. 3766 students responded to our survey across five continents in what we believe is a global first. Our results show that students in all 12 countries report very high levels of 'caseness' on validated measures of psychiatric symptoms and burnout. Rates of substance misuse, often a cause of or coping mechanism for this distress, and identified sources of stress also varied across cultures. Variations are strongly influenced by cultural factors. Further quantitative and qualitative research is required to confirm our results and further delineate the causes for high rates of psychiatric symptoms and burnout. Studies should also focus on the implementation of strategies to safeguard and identify those most at risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".