Factors affecting quality of nurse shift handover in the emergency department
Bibliographic record
Abstract
AIM: The aim of this study was to explore and test factors hypothesized to influence quality of Emergency Department nurse-to-nurse shift handover communication. BACKGROUND: Nurse-to-nurse shift handover communication includes the transfer of information and responsibility for patients at shift change. The unique environment of the Emergency Department, where there is a high degree of patient unpredictability, increased patient volumes and rapid patient turnover, can create challenges for high quality handover communication. There is considerable literature addressing handover communication and factors that influence quality or effectiveness. However, few studies have empirically tested those factors. DESIGN: A quantitative, cross-sectional design was used to test a conceptual model of factors hypothesized to influence quality of handover communication. METHODS: In 2014, data were gathered using surveys mailed to Emergency Department nurses across Ontario, Canada. RESULTS: The final eligible sample was 231 of 576 for an overall response rate of 40.1%. Analysis was performed using backwards elimination stepwise multiple linear regression. Four statistically significant explanatory variables were retained in the final multiple regression model, explaining 34% (p < .0001) of variance in handover quality. Handover quality was increased when patients flowed smoothly through triage, when nurses experienced positive intrusions, in the presence of a positive safety climate and when there were positive relationships between incoming and outgoing nurses. CONCLUSIONS: By understanding those factors that contribute to handover quality, it is possible to develop targeted interventions aimed at improving the quality of Emergency Department nurse-to-nurse shift handover.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".