Exploratory evaluation of surgical skills mentorship program design and outcomes
Bibliographic record
Abstract
INTRODUCTION: There are few opportunities for mentorship of practicing surgeons and no evidence to guide the design of such programs. This study explored outcomes and barriers associated with the design of surgical mentorship programs. METHODS: Interviews were held with organizers, mentors, and protégés of 2 programs. Data from 23 participant interviews and 23 nonparticipant surveys were analyzed thematically. RESULTS: Participation was greater in the program where planning was participatory and mentors visited protégés. Scheduling was a key barrier, and existing relationships enabled mentorship. Most nonparticipants said they were already trained or had no interest in the skill. Mentorship was valued for exchange of tacit knowledge, hands-on learning, and real-time feedback. Mentorship prompted participants to realize gaps in skill; several said they already adopted the new skill, and many were interested in ongoing mentorship. DISCUSSION: Several beneficial outcomes appear to be associated with mentorship, but longitudinal evaluation is required. Telementoring and train-the-trainer models may promote participation in surgical mentorship. Participants suggested that technical training be integrated within pre- and postmentorship education and follow-up. Such programs can only be implemented if issues of sponsorship and funding are addressed.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".