Correlates of physician burnout across regions and specialties: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Health care organizations globally realize the need to address physician burnout due to its close linkages with quality of care, retention and migration. The many functions of health human resources include identifying and managing burnout risk factors for health professionals, while also promoting effective coping. Our study of physician burnout aims to show: (1) which correlates are most strongly associated with emotional exhaustion (EE) and depersonalization (DP), and (2) whether the associations vary across regions and specialties. METHODS: Meta-analysis allowed us to examine a diverse range of correlates. Our search yielded 65 samples of physicians from various regions and specialties. RESULTS: EE was negatively associated with autonomy, positive work attitudes, and quality and safety culture. It was positively associated with workload, constraining organizational structure, incivility/conflicts/violence, low quality and safety standards, negative work attitudes, work-life conflict, and contributors to poor mental health. We found a similar but weaker pattern of associations for DP.Physicians in the Americas experienced lower EE levels than physicians in Europe when quality and safety culture and career development opportunities were both strong, and when they used problem-focused coping. The former experienced higher EE levels when work-life conflict was strong and they used ineffective coping. Physicians in Europe experienced lower EE levels than physicians in the Americas with positive work attitudes. We found a similar but weaker pattern of associations for DP.Outpatient specialties experienced higher EE levels than inpatient specialties when organization structures were constraining and contributors to poor mental health were present. The former experienced lower EE levels when autonomy was present. Inpatient specialties experienced lower EE levels than outpatient specialties with positive work attitudes. As above, we found a similar but weaker pattern of associations for DP. CONCLUSIONS: Although we could not infer causality, our findings suggest: (1) that EE represents the core burnout dimension; (2) that certain individual and organizational-level correlates are associated with reduced physician burnout; (3) the benefits of directing resources where they are most needed to physicians of different regions and specialties; and (4) a call for research to link physician burnout with performance.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.041 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".