Impact of coaching on physician wellness: A systematic review
Bibliographic record
Abstract
Physician wellness is critical for patient safety and quality of care. Coaching has been successfully and widely applied across many industries to enhance well-being but has only recently been considered for physicians. This review aimed to summarize the existing evidence on the effect of coaching by trained coaches on physician well-being, distress and burnout. MEDLINE, Embase, ERIC, PsycINFO and Web of Science were searched without language restrictions to December 21, 2022. Studies of any design were included if they involved physicians of any specialty undergoing coaching by trained coaches and assessed at least one measure along the wellness continuum. Pairs of independent reviewers determined reference eligibility. Risk of bias was assessed using the Cochrane Risk of Bias Tools for Randomized Controlled Trials (RCTs) and for Non-randomized Studies of Interventions (ROBINS-I). Meta-analysis was not possible due to heterogeneity in study design and outcome measures as well as inconsistent reporting. The search retrieved 2531 references, of which 14 were included (5 RCTs, 2 non-randomized controlled studies, 4 before-and-after studies, 2 mixed-methods studies, 1 qualitative study). There were 1099 participants across all included studies. Risk of bias was moderate or serious for non-RCTs, while the 5 RCTs were of lower risk. All quantitative studies reported effectiveness of coaching for at least one outcome assessed. The included qualitative study reported a perceived positive impact of coaching by participants. Evidence from available RCTs suggests coaching for physicians can improve well-being and reduce distress/burnout. Non-randomized interventional studies have similar findings but face many limitations. Consistent reporting and standardized outcome measures are needed.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".