Bedside nurses’ roles in discharge collaboration in general internal medicine: Disconnected, disempowered and devalued?
Bibliographic record
Abstract
Collaboration among nurses and other healthcare professionals is needed for effective hospital discharge planning. However, interprofessional interactions and practices related to discharge vary within and across hospitals. These interactions are influenced by the ways in which healthcare professionals' roles are being shaped by hospital discharge priorities. This study explored the experience of bedside nurses' interprofessional collaboration in relation to discharge in a general medicine unit. An ethnographic approach was employed to obtain an in-depth insight into the perceptions and practices of nurses and other healthcare professionals regarding collaborative practices around discharge. Sixty-five hours of observations was undertaken, and 23 interviews were conducted with nurses and other healthcare professionals. According to our results, bedside nurses had limited engagement in interprofessional collaboration and discharge planning. This was apparent by bedside nurses' absence from morning rounds, one-way flow of information from rounds to the bedside nurses following rounds, and limited opportunities for interaction with other healthcare professionals and decision-making during the day. The disconnection, disempowerment and devaluing of bedside nurses in patient discharge planning has implications for quality of care and nursing work. Study findings are positioned within previous work on nurse-physician interactions and the current context of nursing care.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".