Identifying critical information for nursing handover : designing a nurse to nurse handover form
Bibliographic record
Abstract
Patient handover represents a significant safety risk. At each handover information could be lost, misinterpreted or not well communicated. Patient handover refers to any time responsibility for a patient’s care is transferred from one care provider to another. This process requires succinct communication between the care providers, in this case nurses, to ensure continuity and safety of patient care. A significant handover that occurs daily on any nursing unit is the handover that occurs between nursing shifts: the off-going nurse reports to the oncoming nurse. The purpose of this research was to use an appreciative inquiry process to answer the question: what is the critical information that should be included in a nurse-to-nurse inter-shift report on an acute medical unit at a tertiary, urban teaching hospital? A purposive sample of nurses from the study unit worked together over three separate project group meetings to develop, pilot and refine a new handover form. The 4 D process of the appreciative inquiry method was used including: discover, dream, design and deliver. Thematic analysis was used for each cycle of the apprecitive inquiry process and the main themes found are presented. The central findings from this project include developing a handover form that presents succinct, organized, objective and written information that focuses on the critical events or information from the previous twelve hours and what needs to happen in the next twelve hours. To ensure appropriate use of the form the purpose of the form should be emphasized to all staff and connected to patient safety and continuity of care. In addition, the team discussed implementing a formal and informal feedback process to further encourage appropriate use of the form. Finally, promoting professional accountability to ensure completion of the handover form and accompanying documentation, such as the kardex and careplans.
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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 | 0.034 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".