Improving residents' handovers through just-in-time training for structured communication
Bibliographic record
Abstract
In a recent quality assurance project we learned that nearly half of the handovers we examined were characterized as unsatisfactory by our residents, who provided examples in which their anxiety had been piqued and patient care had been affected. These reports substantiated a growing body of literature on the relationship between the quality of handover and the quality of patient care, so we sought to improve the quality and consistency of the in-hosptial handovers undertaken by our internal medicine residents. Senior residents attended morning report for three consecutive month long blocks and evaluated the quality of the handovers using an observational protocol comprised of 16 aspects of effective handover. During the first block, the resident observed a median of eight of the 16 practices occurring across the 46 handovers, and a large amount of variability. At the beginning of the subsequent block we presented a concise introduction to a structured handover procedure (SBARR). The median quality of the subsequent 33 handovers rose to 11, and the variability decreased considerably. In the next block we refined the SBARR orientation to focus on the errors observed in the previous blocks, and the improvement in the quality and variability was sustained. The minor change, which requires few resources to sustain, had a favourable impact on the quality of our residents' in-hospital handovers.
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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 | 0.012 | 0.037 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".