A systematic review of educational resources for teaching patient handover skills to resident physicians and other healthcare professionals.
Bibliographic record
Abstract
BACKGROUND: As physicians reduce their work hours, transfer of patient care becomes more common; this is a time of heightened risk to patients. Training in patient handover skills may reduce this risk. The objective of this study was to systematically review the literature regarding education models available to teach handovers skills to healthcare professionals. METHODS: Two investigators independently reviewed educational publications for inclusion/exclusion. A third reviewer resolved any disagreement. Included papers contained an educational resource for teaching handover skills to any health profession in any patient population. Papers were rated on a previously described 4 point scale for quality. RESULTS: 1746 papers were identified, of which 12 met the inclusion criteria These studies presented information on educational curricula, simulation technologies and didactic sessions. The most common educational method was simulation or role-playing, which is better received by learners than didactic sessions. Teaching handover practices makes residents feel more confident in their handover, and residents receiving adequate handover are more comfortable with their duties. CONCLUSIONS: Although data are limited, effective training models for handover skills have been described in the literature. Residents and other healthcare practitioners should receive training in handover to improve practitioner comfort and patient care.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".