Trigger videos: a novel application of a tool for surgical faculty development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Trigger videos have occasionally been used in medical education; however, their application to surgical faculty development is novel. We assessed participants' attitudes towards workshops on intraoperative teaching (IOT) that were anchored by trigger videos, and studied whether they could generate discussion-for-learning among surgeons in this workshop setting. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it