Perioperative Teaching and Feedback: How are we doing in Canadian OTL-HNS programs?
Bibliographic record
Abstract
BACKGROUND: Discrepancies between resident and faculty perceptions regarding optimal teaching and feedback during surgery are well known but these differences have not yet been described in Otolaryngology - Head and Neck Surgery (OTL-HNS). The objectives were thus to compare faculty and resident perceptions of perioperative teaching and feedback in OTL-HNS residency programs across Canada with the aim of highlighting potential areas for improvement. METHODS: An anonymous electronic questionnaire was distributed to residents and teaching faculty in OTL-HNS across Canada with additional paper copies distributed at four institutions. Surveys consisted of ratings on a 5-point Likert scale and open-ended questions. Responses among groups were analysed with the Wilcoxon-Mann Whitney test, while thematic analysis was used for the open-ended questions. RESULTS: A total of 143 teaching faculty and residents responded with statistically significant differences on 11 out of 25 variables. Namely, faculty reported higher rates of pre and intra-operative teaching compared to resident reports. Faculty also felt they gave adequate feedback on residents' strengths and technical skills contrary to what the residents thought. Both groups did agree however that pre-operative discussion is not consistently done, nor is feedback consistently given or sought. CONCLUSION: Faculty and residents in OTL-HNS residency programs disagree on the frequency and optimal timing of peri-operative teaching and feedback. This difference in perception emphasizes the need for a more structured approach to feedback delivery including explicitly stating when feedback is being given, and the overall need for better communication between residents and staff.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".