Wellbeing, burnout and substance use amongst medical students: A summary of results from nine countries
Bibliographic record
Abstract
OBJECTIVE: There has been increasing interest in the physical health, mental wellbeing and burnout afflicting medical students over recent years. This paper describes the overall results from phase two of an international study including a further nine countries across the world. METHODS: We sampled large groups of medical students in nine countries at the same time and with exactly the same method in order to aid direct comparison of demographics, burnout and mental wellbeing through validated instruments. RESULTS: A total of 4,942 medical students from these countries participated in this study. Around 68% of respondents screened positive for mild psychiatric illness using the General Health Questionnaire-12. Around 81% and 78% of respondents were found to be disengaged or exhausted respectively using the Oldenburg Burnout Inventory. Around 10% were found to be CAGE positive and 14% reported cannabis use. The main source of stress reported by medical students was their academic studies, followed by relationships, financial difficulties and housing issues. CONCLUSION: Cultural, religious and socioeconomic factors within each country are important and understanding their effects is fundamental in developing successful local, regional and national initiatives. Further quantitative and qualitative research is required to confirm our results, clarify their causes and to develop appropriate preventative strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".