Collaboration among nurses when transitioning older adults between hospital and community settings: a scoping review
Bibliographic record
Abstract
BACKGROUND: The transitioning of older patients between healthcare sectors requires the provision of high-quality nursing care. Collaboration among nurses is identified as an essential element of transitional care, yet nurse-nurse collaboration has received little attention. AIM: The aim of this study was to examine the extent, range and nature of nurse-nurse collaboration when transitioning older patients between hospital and community settings, and to identify gaps in the literature. METHODS: Arksey and O'Malley's (International Journal of Social Research Methodology, 8, 2005 and 19) framework was used to undertake a scoping review to answer the research questions: how do nurses collaborate together when transitioning older patients from hospital to community settings and what are the facilitators, barriers and outcomes of nurse-nurse collaboration when transitioning older patients between sectors? The Nurse-Nurse Collaboration Scale (NNCS) subdomains informed the identification of selected studies. RESULTS: Twelve papers were included with most coming from Scandinavian countries and the majority using qualitative methodologies. Communication, coordination and professionalism were found to be both facilitators and barriers of nurse-nurse collaboration. Gaps in the literature included conflict management, and the outcomes of collaboration which was only reported in one study. CONCLUSIONS: The findings indicate there is limited study of collaboration among nurses when transitioning older patients between hospital and community settings. Future research should address the impact of conflict on nurses working in collaborative practice as well as conducting intervention studies to examine the outcomes of nurse-nurse collaboration.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".