Efficacy of Interventions to Reduce Resident Physician Burnout: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Studies report high burnout prevalence among resident physicians, with little consensus on methods to effectively reduce it. Objective This systematic literature review explores the efficacy of interventions in reducing resident burnout. Methods PubMed, Embase, and Web of Science were searched using these key words: burnout and resident, intern, or residency. We excluded review articles, editorials, letters, and non–English-language articles. We abstracted data on study characteristics, population, interventions, and outcomes. When appropriate, data were pooled using random effects meta-analysis to account for between-study heterogeneity. Study quality was assessed using Newcastle-Ottawa Scale (cohort studies) and Jadad scale (randomized control trials [RCTs]). Results Of 1294 retrieved articles, 19 (6 RCTs, 13 cohort studies) enrolling 2030 residents and examining 12 interventions met criteria, with 9 studying the 2003 and 2011 Accreditation Council for Graduate Medical Education (ACGME) duty hour restrictions. Work hour reductions were associated with score decrease (mean difference, −2.73; 95% confidence interval (CI) −4.12 to −1.34; P < .001) and lower odds ratio (OR) for residents reporting emotional exhaustion (42%; OR = 0.58; 95% CI 0.43–0.77; P < .001); a small, significant decrease in depersonalization score (−1.73; 95% CI −3.00 to −0.46; P = .008); and no effect on mean personal accomplishment score (0.93; 95% CI −0.19–2.06; P = .10) or for residents with high levels of personal accomplishment (OR = 1.01; 95% CI 0.67–1.54; P = .95). Among interventions, self-care workshops showed decreases in depersonalization scores, and a meditation intervention reduced emotional exhaustion. Conclusions The ACGME work hour limits were associated with improvement in emotional exhaustion and burnout.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".