Bibliographic record
Abstract
OBJECTIVE: Many contemporary acute care facilities lack safe and effective clinical handover practices resulting in patient transitions that are vulnerable to discontinuities in care, medical errors, and adverse patient safety events. This article is intended to supplement existing handover improvement literature by providing practical guidance for leaders and managers who are seeking to improve the safety and the effectiveness of clinical handovers in the acute care setting. METHODS: A 4-stage change model has been applied to guide the application of strategies for handover improvement. Change management and quality improvement principles, as well as concepts drawn from safety science and high-reliability organizations, were applied to inform strategies. RESULTS: A model for handover improvement respecting handover complexity is presented. Strategies targeted to stages of change include the following: 1. Enhancing awareness of handover problems and opportunities with the support of strategic directions, accountability, end user involvement, and problem complexity recognition. 2. Identifying solutions by applying and adapting best practices in local contexts. 3. Implementing locally adapted best practices supported by communication, documentation, and training. 4. Institutionalizing practice changes through integration, monitoring, and active dissemination. Finally, continued evaluation at every stage is essential. CONCLUSIONS: Although gaps in handover process and function knowledge remain, efforts to improve handover safety and effectiveness are still possible. Continued evaluation is critical in building this understanding and to ensure that practice changes lead to improvements in patient safety, organizational effectiveness, and patient and provider satisfaction. Through handover knowledge building, fundamental changes in handover policies and practices may be possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".