Burnout in medical students before residency: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Applying the concept of burnout to medical students before residency is relatively recent. Its estimated prevalence varies significantly between studies. Our objective was to estimate the prevalence of burnout in medical students worldwide. METHODS: We systematically searched Medline for English-language articles published between January 1, 2010 and December 31, 2017. We selected all the original studies about the prevalence of burnout in medical students before residency, using validated questionnaires for burnout. Statistical analyses were conducted using the OpenMetaAnalyst software. RESULTS: Prevalence of current burnout was extracted from 24 studies encompassing 17,431 medical students. Among them, 8060 suffered from burnout and we estimated the prevalence to be 44.2% [33.4%-55.0%]. The information about the prevalence of each subset of burnout dimensions was given in nine studies including 7588 students. Current prevalence was estimated to be 40.8% for 'emotional exhaustion' [32.8%-48.9%], 35.1% [27.2%-43.0%] for 'depersonalization' and 27.4% [20.5%-34.3%] for 'personal accomplishment'. There is no significant gender difference in burnout. The prevalence of burnout is slightly different across countries with a higher prevalence in Oceania and the Middle East than in other continents. CONCLUSIONS: The results of this meta-analysis suggest that one student out of two is suffering from burnout, even before residency. Again, our findings highlight the high level of distress in the medical population. These results should encourage the development of preventive 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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".