Nurses' experience of handoffs on four Canadian medical and surgical units: A shared accountability for knowing and safeguarding the patient
Bibliographic record
Abstract
AIMS: To explore nurses' experience and describe how they manage various contextual factors affecting the nurse-to-nurse handoff at change of shift. DESIGN: Qualitative descriptive study. METHODS: A convenience sample of 51 nurses from four medical and surgical care units at a university-affiliated hospital in Montreal, Canada, participated in one of the 19 focus group interviews from November 2017 to January 2018. Data were analysed through a continuous and iterative process of thematic analysis. RESULTS: Analysis of the data generated a core theme of 'sharing accountability for knowing and safeguarding the patient' that is achieved through actions related to nurses' role in the exchange. Specifically, the outgoing nurse takes actions to ensure continuity of care when letting go, and the incoming nurse takes actions to provide seamless care when taking over. In both roles, nurses navigate each handoff juncture by mutually adjusting, ensuring attentiveness, managing judgements, keeping on track, and venting and debriefing. Handoff is also shaped by contextual conditions related to handoff norms and practices, the nursing environment, individual nurse attributes and patient characteristics. CONCLUSIONS: This study generated a conceptualization of nurses' roles and experience that details the relationship among the elements and conditions that shape nurse-to-nurse handoffs. IMPACT: Nursing handoff involves the communication of patient information and relational behaviours that support the exchange. Although many factors are known to influence handoffs, little was known about nurses' experience of dealing with these at the point of care. This study contributed a comprehensive conceptualization of nursing handoff that could be useful in identifying areas for quality improvement and guiding future educational efforts.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".