The role of nurses in inpatient geriatric rehabilitation units: A scoping review
Bibliographic record
Abstract
AIMS: (1) To review and synthesize research on the contributions of nurses to rehabilitation in inpatient geriatric rehabilitation units (GRUs), and (2) to compare these reported contributions to the domains of international rehabilitation nursing competency models. The roles and contributions of nurses (e.g. Registered Practical Nurses, Registered Nurses and Licensed Practical Nurses) in GRUs are non-specific, undervalued, undocumented and unrecognized as part of the formal Canadian rehabilitation process. DESIGN: Arksey and O'Malley's methodological framework for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines were used. METHODS: Six databases were searched for relevant literature: MEDLINE, PsychINFO, CINAHL, EMBASE, SCOPUS and Nursing and Allied Health. English articles were included if they examined nursing roles or contributions to inpatient geriatric rehabilitation. Integrated synthesis was used to combine the qualitative and quantitative data, and thematic analysis was used for coding. Three sets of international competency models were amalgamated to explore how different nurse roles in geriatric rehabilitation were portrayed in the included literature. RESULTS: Eight studies published between 1991 and 2020 were included in the review. Five main geriatric rehabilitation nursing roles were generated from synthesis of the domains of international rehabilitation nursing competency models: conserver, supporter, interpreter, coach and advocate. CONCLUSIONS: Nurses working in inpatient geriatric rehabilitation are recognized more for their role in conserving the body than their roles in supporting, interpreting, coaching and advocacy. Interprofessional team members appear to be less sure of the nurses' role in the rehabilitation unit. Nurses themselves do not acknowledge the unique rehabilitation aspects of care for older adults. Enhancing formal education, or adding continuing education courses, to facilitate role clarity for nurses in geriatric rehabilitation could improve nurses' and interprofessional healthcare team members' understandings of the possible contributions of nurses working in rehabilitation settings.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| 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".