Enhancing Surgical Training Through Coaching: A Systematic Review of Self-Determination Theory in Surgical Education
Bibliographic record
Abstract
Introduction.Surgical coaching is an emerging teaching method that promotes professional development.However, few interventions are grounded in educational theory.This systematic review evaluated how surgical coaching aligns with Self-Determination Theory (SDT), a framework emphasizing autonomy, competence, and relatedness, and whether SDT alignment is associated with improved educational outcomes. Methods.A systematic review was conducted in November 2024 across multiple databases using PRISMA guidelines.Eligible studies evaluated surgical coaching interventions in the U.S. or Canada, included residents or attendings, and reported educational outcomes.Study quality, educational impact, and SDT alignment were assessed using the MERSQI, Kirkpatrick's framework, and Gillison et al.'s coding framework, respectively.Results.Fifteen studies met inclusion criteria.Coaching models included faculty-led (n = 10), peer-led (n = 6), and hybrid (n = 1) formats.SDT strategies were coded 87 times: competencesupportive strategies were most common (n = 43), followed by autonomy (n = 31) and relatedness (n = 13).Interventions using video review or validated skill assessments (e.g., OSATS, GOALS) had significantly higher MERSQI scores (p <0.05).Six randomized controlled trials demonstrated improved technical performance.Peer-led and hybrid models showed greater SDT alignment.Conclusions.SDT-aligned coaching programs, particularly those using structured curricula, validated tools, and milestone-based feedback, may enhance surgical education by fostering motivation and skill development.Peer-led and video-based models showed promise for supporting autonomy and relatedness.SDT may serve not only as a theoretical foundation, but as a practical framework to improve coaching design, reinforce psychological safety, and promote individualized, competency-based growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".