Development and pilot evaluation of a structured curriculum for surgical handover
Bibliographic record
Abstract
BACKGROUND: Effective handover communication is a core professional competency in graduate medical education, yet very few junior doctors working in surgery receive formal training. A structured curriculum was developed and piloted to teach best practices in surgical handover, based on a recognised curricular framework. METHODS: The study was carried out at two academic tertiary hospitals in Dublin, Ireland. Interns attending mandatory weekly teaching sessions participated in a 60-minute intervention combining didactic teaching, video demonstration, small group simulation, and facilitated discussion. Self-reported confidence in delivering and participating in handover was assessed using pre- and post-session surveys. Post-session feedback on curriculum content and format was also collected. RESULTS: A total of 59 interns attended the teaching sessions, with 35 providing paired pre- and post-session data. Self-reported confidence significantly improved across all assessed domains assessed (p<0.001), including confidence in handing over to peers and senior colleagues, asking clarifying questions during handover, and providing a summary or 'readback' at the end of handover. Feedback from 46 participants indicated that the session was well-received, with video demonstrations and simulated practice rated most helpful. Didactic teaching and peer feedback were rated least helpful. A majority (76.1%; n=35) reported that the session would lead to changes in their handover practice. CONCLUSIONS: This pilot study showed that a simulation-based curriculum is effective in improving interns' self-reported confidence in delivering and receiving surgical handover. The teaching session was well-received, easily integrated into existing institutional infrastructure, and required minimal resources to carry out.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".