Achieving the ‘perfect handoff’ in patient transfers: building teamwork and trust
Bibliographic record
Abstract
AIMS: To use the philosophy and methodology of Appreciative Inquiry (AI) in the investigation of unit to unit transfers to determine aspects which are working well and should be incorporated into standard practice. BACKGROUND: Handoffs can result in threats to patient safety and an atmosphere of distrust and blaming among staff can be engendered. As the majority of handoffs go well, an alternative is to build on successful handoffs. EVALUATION: The AI methodology was used to discover what was currently working well in unit to unit transfers. The data from semi-structured interviews that were conducted with staff, patients, and family informed structural process improvements. KEY ISSUES: Themes extracted from the interviews focused on the situational variables necessary for the perfect transfer, the mode and content of transfer-related communication, and important factors in communication with the patient and family. CONCLUSIONS: This project was successful in demonstrating the usefulness of AI as both a quality improvement methodology and a strategy to build trust among key stakeholders. IMPLICATIONS FOR NURSING MANAGEMENT: Giving staff members the opportunity to contribute positively to process improvements and share their ideas for innovation has the potential to highlight expertise and everyday accomplishments enhancing morale and reducing conflict.
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".