Enhancing surgical education using video playback: A case study on the influence of video playback on the nature and experience of feedback between supervising surgeons and surgical residents.
Bibliographic record
Abstract
Introduction: Feedback about intraoperative performance remains a cornerstone of surgical training, and yet perceptions of feedback quality by supervising surgeons and surgical residents differ. Video playback offers one potential method for more effective feedback to surgical residents. More research is needed to better understand this tool. This study explores the nature of instructional interactions and feedback in the operating room and when using video playback during post-operative review. Methods: Three surgical residents and five supervising surgeons were involved in six laparoscopic cases. Data collected included the intraoperative and video playback conversations between the resident and supervising surgeon and semi-structured interviews exploring the resident and supervising surgeon experience of using video playback as a feedback tool. A combination of deductive, inductive and hermeneutic analytic approaches was used. Data was triangulated to develop the big ideas. Results: Analysis of the intraoperative verbal interactions identified that the majority of these interactions (48%) were instrumental and didactic in nature. By contrast, most interactions during video playback were teaching in nature (65%) and the sessions were dialogic. Video playback was perceived as a valuable tool by residents and supervising surgeons for feedback on surgical performance. Participants valued the exact visual representation of the surgical performance provided by the video as it provided cues for specific feedback. They also perceived the video playback environment to be calm and lower in cognitive load, allowing for optimized learning. Both the residents and supervising surgeons found the video was a useful tool to self- reflect on their own performance. These results were further explored through the lenses of social cognitive theory and cognitive load theory. Conclusions: The two big ideas which emerged from this research were: the environment in which video playback occurred contributed positively to the feedback experience, and feedback using video playback is a dialogic critical visual review of the performance. Video playback provided a surgical learning opportunity outside of the operating room which maintained the contextual verity of the surgical case performed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".