Learning objectives for medical student teaching in the operating theatre: surgeons’ experiences and factors influencing operative participation
Bibliographic record
Abstract
Introduction The operating theatre offers a unique and rich learning environment for medical students. Recent evidence indicates that medical student attendance at theatre teaching is low. There is limited evidence of the perceptions of the teacher in this environment. The aim of this study was to characterise the experiences of consultant surgeons when teaching medical students in theatre. Methods A 22-item self-administered survey was distributed to all surgical consultants in 5 specialties involved in the delivery of medical education at 5 hospitals associated with a single UK medical school. Responses were anonymous. Results Questionnaires were returned by 99 (64%) of the 154 surgical consultants. The majority (n=80, 80%) enjoyed teaching medical students in the operating theatre. Two-thirds (64%) set learning objectives for their students but a quarter (25%) did not feel that they knew what the curriculum expected students to learn. Two-thirds (65%) of the consultants stated that they would not let students perform procedures in theatre. Respondents suggested a total of 266 generic objectives that they would want medical students to learn. These were grouped by theme, with the most commonly cited learning objectives being clinical knowledge (44%), skills and procedures (29%), awareness of the room environment (18%) and appreciation of the patient journey (9%). Conclusions Surgeons are largely positive about delivering medical student teaching in the theatre environment. Teacher training with clearer communication between medical school faculty and surgical teachers may help facilitate objective setting to ensure students derive maximum benefit from this unique teaching environment.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".