Burnout in medical students before residency: A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it