An Evidence-Based, Nursing Handover Standard for a Multisite Public Hospital in Switzerland: Web-Based, Modified Delphi Study
Bibliographic record
Abstract
BACKGROUND: Ineffective communication procedures create openings for errors when health care professionals fail to transfer complete, consistent information. Deficient or absent clinical handovers, or failures to transfer information, responsibility, and accountability, can have severe consequences for hospitalized patients. Clinical handovers are practiced every day, in many ways, in all institutional health care settings. OBJECTIVE: This study aimed to design an evidence-based, nursing handover standard for inpatients for use at shift changes or internal transfers between hospital wards. METHODS: We carried out a modified, multiround, web-based, Delphi data collection survey of an anonymized panel sample of 264 nurse experts working at a multisite public hospital in Switzerland. Each survey round was built on responses from the previous one. The surveys ended with a focus group discussion consisting of a randomly selected panel of participants to explain why items for the evidence-based clinical nursing handover standard were selected or not selected. Items had to achieve a consensus of ≥70% for selection and inclusion. RESULTS: The study presents the items selected by consensus for an evidence-based nursing handover standard for inpatients for use at shift changes or internal transfers. It also presents the reasons why survey items were or were not included. CONCLUSIONS: This modified Delphi survey method enabled us to develop a consensus- and evidence-based nursing handover standard now being trialed at shift changes and the internal transfers of inpatients at our multisite public hospital in Switzerland.
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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 | 0.062 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".