Clinical Improvement Interventions for Residents and Practicing Physicians: A Scoping Review of Coaching and Mentoring for Practice Improvement
Bibliographic record
Abstract
BACKGROUND: Graduate medical education (GME) bodies are beginning to mandate coaching as an integral part of the learning process, in addition to current requirements for mentorship. Once an emergency medicine physician transitions beyond graduate training, there is no requirement and little focus on coaching as a method of improving or maintaining clinical practice. Our objective was to understand and describe the current state of the published literature with regard to the use of coaching and mentorship for both GME and practicing physicians. METHODS: We conducted a structured review of the literature through PubMed and Google Scholar and included all articles applying coaching or mentorship modalities to GME trainees or practicing physicians. A Google Form was used for standardized data abstraction. Data were collected pertaining to the settings of intervention, the nature of the intervention, its effect, and its resource requirements. RESULTS: A total of 3,546 papers were isolated during the literature review. After exclusion, 186 underwent full-text review by the authors of which 126 articles were included in the final data analysis. Eighty-two articles (65%) pertained to mentorship and 14 (11%) to coaching; the remainder of the articles discussed a combination or variation of these two concepts. Fifty-three (42%) articles were descriptive studies and 35 (28%) were narrative reviews or commentaries. Forty-seven (37%) articles originated from within surgical specialties and coaching was most commonly applied to procedural or manual skills with 22 (17%) instances among all studies. CONCLUSIONS: Most literature on coaching and mentorship is descriptive or narrative, and few papers are in the specialty of emergency medicine. Most interventions are limited to single instances of coaching or mentorship without longitudinal application of the intervention. There is an important need to study and publish further evidence on coaching interventions.
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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 | 0.032 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".