Nursing transfer of accountability at the bedside: partnering with patients to pilot a new initiative in Ontario community hospitals
Bibliographic record
Abstract
The transfer of accountability (TOA) for a patient from one nurse to another at change of shift is an important opportunity to exchange essential patient care information, as well as to enhance the safety and quality of patient care. This study was undertaken to explore nurses’, patients’ and family members’ perceptions associated with the implementation of bedside nurse to nurse TOA. Focus groups were conducted pre-implementation (two with nurses and two with patients and family members) and post-implementation (six with nurses and two with patients and family members). The focus groups were audio-recorded, transcribed and analysed using directed content analysis. Findings were divided into positive outcomes and challenges to bedside nurse to nurse TOA. Positive outcomes included increased patient safety, more informed patients more consistent use of whiteboards in the patient rooms, better engagement with family via the whiteboard and increased family involvement, confirmation of information between nurses, increased accountability between nurses, and personal introduction/icebreaker of the new nurse. The inclusion of the Patient Partners on the project team was a key success factor for the project. Challenges included a perception of lengthened time required for TOA and increased workload, lack of privacy and potential breaches of confidentiality, patient fear and lack of comprehension, lack of clarity in TOA processes, and inconsistent application of the procedures. Hospital administrators and nurse leaders can use these findings to anticipate and understand change associated with bedside TOA as seen by both nurses and patients/families.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".