Addressing the physician burnout epidemic with resilience curricula in medical education: a systematic review
Bibliographic record
Abstract
BACKGROUND: A variety of stressors throughout medical education have contributed to a burnout epidemic at both the undergraduate medical education (UGME) and postgraduate medical education (PGME) levels. In response, UGME and PGME programs have recently begun to explore resilience-based interventions. As these interventions are in their infancy, little is known about their efficacy in promoting trainee resilience. This systematic review aims to synthesize the available research evidence on the efficacy of resilience curricula in UGME and PGME. METHODS: We performed a comprehensive search of the literature using MEDLINE, EMBASE, PsycINFO, Educational Resources Information Centre (ERIC), and Education Source from their inception to June 2020. Studies reporting the effect of resilience curricula in UGME and PGME settings were included. A qualitative analysis of the available studies was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Risk of bias was assessed using the ROBINS-I Tool. RESULTS: Twenty-one studies met the inclusion criteria. Thirteen were single-arm studies, 6 quasi-experiments, and 2 RCTs. Thirty-eight percent (8/21; n = 598) were implemented in UGME, while 62 % (13/21, n = 778) were in PGME. There was significant heterogeneity in the duration, delivery, and curricular topics and only two studies implemented the same training model. Similarly, there was considerable variation in curricula outcome measures, with the majority reporting modest improvement in resilience, while three studies reported worsening of resilience upon completion of training. Overall assessment of risk of bias was moderate and only few curricula were previously validated by other research groups. CONCLUSIONS: Findings suggest that resilience curricula may be of benefit to medical trainees. Resilience training is an emerging area of medical education that merits further investigation. Additional research is needed to construct optimal methods to foster resilience in medical education.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".