“Influential” Intraoperative Educators and Variability of Teaching Styles
Bibliographic record
Abstract
OBJECTIVES: Academic surgeons manage their role as intraoperative educators in a variety of ways. Such variability is neither idiosyncratic nor is there a single best approach. This study sought to explore the practices of surgeons deemed influential by their residents, allowing insight into a variety of potentially effective practices. PARTICIPANTS: Constructivist grounded theory guided data collection and analysis. Data sources included surveys from senior surgical residents (PGY3-6) and recent graduates from an academic hospital in Canada (36% response rate), intraoperative observations of teaching interactions, and semi-structured interviews with observed surgeons. Rigour was supported by data triangulation, constant comparison, and collection to theoretical sufficiency. DESIGN: We developed a framework grouping effective teaching into three overlapping approaches: exacting, empowering, and fostering. The approaches differ based on the level of independence granted and the degree of expectation placed on individual residents. Each demonstrates different strategies for balancing the multiple supervisory roles and patient care obligations faced by academic surgeons. We also identified strategies that could be used across approaches to enhance learning. CONCLUSIONS: For surgical educators seeking to improve upon the quality of the intraoperative supervision they provide, frameworks such as this may serve as models of effective supervision. Enhancing surgeons' knowledge of proven strategies, combined with reflecting on how they teach and how they balance responsibilities to patients and trainees, may allow them to broaden their educational practice.
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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 | 0.016 | 0.085 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".