Coaching in Surgical Education: A Systematic Review.
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to review the coaching literature to (1) characterize the criteria integral to the coaching process, specifically in surgery, and (2) describe how these criteria have been variably implemented in published studies. BACKGROUND: Coaching is a distinct educational intervention, but within surgery the term is frequently used interchangeably with other more established terms such as teaching and mentoring. METHODS: A systematic search was performed of the MEDLINE and Cochrane databases to identify studies that used coach/coaching as an intervention for surgeons for either technical or nontechnical skills. Study quality was evaluated using the Medical Education Research Study Quality Instrument (MERSQI). RESULTS: A total of 2280 articles were identified and after screening by title, abstract and full text, 35 remained. Thirteen coaching criteria (a-m) were identified in 4 general categories: 1. overarching goal (a. refine performance of an existing skill set), 2. the coach (b. trusting partnership, c. avoids assessment, d. 2-way communication), the coachee (e. voluntary participation, f. self-reflection, g. goal setting, h. action plan, i. outcome evaluation), and the coach-coachee rapport (j. coaching training, k. structured coaching model, l. non-directive, m. open ended questions). Adherence to these criteria ranged from as high of 73% of studies (voluntary participation of coach and coachee) to as low as 7% (use of open-ended questions). CONCLUSIONS: Coaching is being used inconsistently within the surgical education literature. Our hope is that with establishing criteria for coaching, future studies will implement this intervention more consistently and allow for better comparison and generalization of results.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| 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) | 0.005 | 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".