Developing an Evidence-Based Nursing Handover Standard for a Multi-Site Public Hospital in Switzerland: Protocol for a Web-Based, Modified Delphi Study
Bibliographic record
Abstract
BACKGROUND: Poor communication processes create opportunities for errors when caregivers fail to transfer complete and consistent information. Inadequate or nonexistent clinical handovers or failures to transfer information, responsibility, and accountability can have dire consequences for hospitalized patients. Clinical handover is practiced every day, in a multitude of ways, in all health care settings. OBJECTIVE: The goal of this study is to build a consensus, evidence-based nursing handover standard for inpatients during shift changes or internal transfers between hospital wards. The study will be based on papers published by Slade et al. METHODS: This protocol describes a modified Delphi data-collection survey involving a targeted panel sample of 300 nurse experts. A multi-round survey will select an anonymous panel from a multi-site public hospital in Switzerland. Each survey stage will be described and will build on the previous one. The study will end with a focus group discussion involving a randomly selected panel to explain why items for the evidence-based clinical nursing handover standard were accepted or not accepted. An item must achieve a consensus of ≥70% for inclusion. RESULTS: The present study's expected outcome is a consensus-built, evidence-based nursing handover standard for inpatients during shift changes or internal transfers between the wards of a multi-site public hospital in Switzerland. CONCLUSIONS: This survey will enable us to develop an evidence-based nursing handover standard for use during shift changes and internal inpatient transfers in a multi-site public hospital in Switzerland. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15910.
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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 | 0.144 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".