Trigger Videos: A Novel Application of A Tool For Surgical Faculty Development
Bibliographic record
Abstract
Abstract Background Trigger videos have occasionally been used in medical education; however, their application to surgical faculty development is novel. We assessed whether workshops designed to improve surgeons’ intraoperative teaching (IOT), anchored by trigger videos, were useful and effective in inspiring surgeons to improve their IOT.Methods Surgeons from multiple specialties attended one of six faculty development workshops where IOT trigger videos were shown and discussed during break-out sessions. Participants completed questionnaires to 1) evaluate videos via survey and feedback, and 2) identify adoptable and discardable IOT techniques. Teaching techniques were collated to identify planned IOT changes and survey data and feedback were analyzed.Results A total of 135 surgeons identified 292 adoptable and 202 discardable IOT techniques based on trigger videos and discussions, and 94% of participants reported that the trigger videos were useful and encouraged them to discuss and consider new IOT techniques in their own practice.Conclusions Participants reported that the trigger videos were useful and motivating. Surgeons critically reflected on IOT during the sessions, identifying numerous adoptable and discardable techniques relevant to their own teaching styles. Trigger videos can be a valuable tool for surgical faculty development and can be tailored to other medical specialties.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".