Global prevalence of burnout among postgraduate medical trainees: a systematic review and meta-regression
Bibliographic record
Abstract
BACKGROUND: Burnout among postgraduate medical trainees (PMTs) is increasingly being recognized as a crisis in the medical profession. We aimed to establish the prevalence of burnout among PMTs, identify risk and protective factors, and assess whether burnout varied by country of training, year of study and specialty of practice. METHODS: We systematically searched MEDLINE, Embase, PsycINFO, the Cochrane Database of Systematic Reviews, Web of Science and Education Resources Information Center from their inception to Aug. 21, 2018, for studies of burnout among PMTs. The primary objective was to identify the global prevalence of burnout among PMTs. Our secondary objective was to evaluate the association between burnout and country of training, year of study, specialty of training and other sociodemographic factors commonly thought to be related to burnout. We employed random-effects meta-analysis and meta-regression techniques to estimate a pooled prevalence and conduct secondary analyses. RESULTS: In total, 8505 published studies were screened, 196 met eligibility and 114 were included in the meta-analysis. The pooled prevalence of burnout was 47.3% (95% confidence interval 43.1% to 51.5%), based on studies published over 20 years involving 31 210 PMTs from 47 countries. The prevalence of burnout remained unchanged over the past 2 decades. Burnout varied by region, with PMTs of European countries experiencing the lowest level. Burnout rates among medical and surgical PMTs were similar. INTERPRETATION: Current wellness efforts and policies have not changed the prevalence of burnout worldwide. Future research should focus on understanding systemic factors and leveraging these findings to design interventions to combat burnout. STUDY REGISTRATION: PROSPERO no. CRD42018108774.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".