Factors Influencing the Quality of Emergency Department Nurse Shift Handover
Bibliographic record
Abstract
Nurse-to-nurse shift handover communication is an essential exchange of information that occurs at shift change with the purpose of ensuring that incoming nurses have necessary information to take responsibility for their patients and to provide high quality, safe care. Poor quality shift handover has been associated with adverse outcomes such as incorrect treatment, delays in diagnosis, increased length of stay, and both nurse and patient dissatisfaction. Despite an increase in the amount of handover related literature, little is known about factors that influence quality of nurse-to-nurse shift handover. \nThe Emergency Department (ED) environment presents unique challenges for high quality handover communication as a result of unpredictability, increased volumes and rapid patient turnover. The purpose of this study was to test and refine a conceptual model of 18 factors hypothesized to influence quality of nurse-to-nurse shift handover communication in the ED.\nThis study was conducted using a cross-sectional survey design. A total of 650 ED nurses across the Province of Ontario were invited to participate in this study. The survey included questions about demographic information as well as items and instruments to measure concepts such as staffing, triage, relationships, safety climate, interruptions, job stress, fatigue and handover format. The hypothesized conceptual model was tested using backwards stepwise multiple regression with data from a final sample of 227 participants. \nFollowing multiple regression analysis, four statistically significant predictors were retained in the final model. Together, triage flow, intrusions, safety climate and relationships explained 34% of the variance in handover quality (p
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".