Nurses' Descriptions of Interdisciplinary Interactions in Stroke and Geriatric Rehabilitation Units: A Case Example of the Registered Practical Nurse
Bibliographic record
Abstract
AIM: To analyse how nurses describe their interactions with other interdisciplinary team members within stroke and geriatric rehabilitation. DESIGN: A secondary analysis of cross-sectional ethnographic interview data was conducted using Elo and Kyngäs' (2008) deductive content analysis. METHODS: Between April 12 and July 25, 2022, semi-structured interviews were conducted with 31 registered practical nurses recruited through convenience sampling from three tertiary hospital sites in Southwestern Ontario. Interview transcripts were reviewed to identify described interactions between nurses and interdisciplinary team members and were coded for: who were the interdisciplinary team member(s) involved; what content was addressed; and where, when, and why the interaction occurred. RESULTS: Categories representing how nurses describe their interactions with interdisciplinary team members were as follows: (1) arising from the unique roles owned by either the nurse or interdisciplinary team member(s); (2) requiring open communication to achieve patient goals and improve patient care; (3) occurring within what is perceived to be either the therapists' or nurses' physical space; and (4) contributing to supportive team environments that are mutually beneficial. CONCLUSIONS: While nurses view other interdisciplinary team members as 'owning' certain roles and physical spaces within rehabilitation, they also recognised their 'owned' spaces and roles. Unique contributions of all team members were valued as necessary to provide holistic, person-centred patient care and positive team-based support. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses' descriptions of their interactions with interdisciplinary team members demonstrated their essential contributions to team-based patient care and acknowledged nurse contributions to the rehabilitation process for patients. IMPACT: Findings elucidate the nature of interprofessional interactions and 'ownership' within the rehabilitation process. Results are beneficial for policymakers, educators, and healthcare organisations aiming to optimise the nursing role within rehabilitation spaces. REPORTING METHOD: The Consolidated Criteria for Reporting Qualitative Research Checklist (COREQ). PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".