Understanding nursing on an acute stroke unit: perceptions of space, time and interprofessional practice
Bibliographic record
Abstract
AIM: This paper is a report of a study conducted to uncover nurses' perceptions of the contexts of caring for acute stroke survivors. BACKGROUND: Nurses coordinate and organize care and continue the rehabilitative role of physiotherapists, occupational therapists and social workers during evenings and at weekends. Healthcare professionals view the nursing role as essential, but are uncertain about its nature. METHOD: Ethnographic fieldwork was carried out in 2006 on a stroke unit in Canada. Interviews with nine healthcare professionals, including nurses, complemented observations of 20 healthcare professionals during patient care, team meetings and daily interactions. Analysis methods included ethnographic coding of field notes and interview transcripts. FINDINGS: Three local domains frame how nurses understand challenges in organizing stroke care: 1) space, 2) time and 3) interprofessional practice. Structural factors force nurses to work in exceptionally close quarters. Time constraints compel them to find novel ways of providing care. Moreover, sharing of information with other members of the team enhances relationships and improves 'interprofessional collaboration'. The nurses believed that an interprofessional atmosphere is fundamental for collaborative stroke practice, despite working in a multiprofessional environment. CONCLUSION: Understanding how care providers conceive of and respond to space, time and interprofessionalism has the potential to improve acute stroke care. Future research focusing on nurses and other professionals as members of interprofessional teams could help inform stroke care to enhance poststroke outcomes.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".