Exploring the nature and focus of feedback when using video playback in gynecology laparoscopy training
Bibliographic record
Abstract
BACKGROUND: Feedback about intraoperative performance remains a cornerstone of surgical training. Video playback offers one potential method for more effective feedback to surgical residents. More research is needed to better understand this method. This study explores the nature of instructional interactions and feedback in the operating room (OR) and when using video playback during post-operative review in obstetrics and gynecology (OBGYN) training. METHOD: This case study occurred between September 2016 and February 2017. Three OBGYN residents and five OBGYN supervising surgeons were involved in six laparoscopic cases. Intraoperative and video playback dialogues were recorded and analysed, the former deductively using codes identified from published literature, and the latter both deductively, using the same codes, and inductively, with codes that emerged from the data during analysis. RESULTS: 1090 intraoperative interactions were identified within 376 minutes of dialogue. Most interactions were didactic, instructing the resident how to use an instrument to perform a task. Deductive analysis of postoperative video playback review identified 146 interactions within 155 minutes. While the most common interaction type remained didactic, a teaching component was included more often. It became apparent that deductive analysis using the intraoperative codes did not adequately capture the nature and focus of feedback during video playback. Hermeneutic phenomenological analysis identified more dialogic video playback sessions with more resident-initiated questions and reflection. CONCLUSIONS: This study demonstrates that the nature of feedback during video playback is fundamentally different from that in the OR, offering a greater potential for collaborative and improved learning.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".