The Role and Contributions of Nurses in Stroke Rehabilitation Units: An Integrative Review
Bibliographic record
Abstract
Nurses’ contributions to stroke rehabilitation have been viewed as pivotal, but therapeutically nonspecific. This integrative review synthesized empirical literature on the roles and contributions of nurses to inpatient stroke rehabilitation to answer three research questions: (a) What specific skills or tasks have been identified as the roles and contributions of nurses to inpatient stroke rehabilitation? (b) How do nurses perform these skills/tasks to support and promote inpatient stroke rehabilitation and recovery? and (c) What factors have been identified to impact nurses’ working conditions on inpatient stroke rehabilitation units? A systematic search of multiple electronic databases retrieved seven studies which provided significant context and examples to these questions. What nurses do in practice included, for example, maximizing patients’ independence in performing daily activities, preventing harm, and preserving integrity. How nurses perform their therapeutic roles included teaching, coaching, coordination, management, advocacy, collaboration. Factors that impact nurses’ working conditions consisted of time, resources, and knowledge. This review demonstrates our current understanding of nurses’ contributions to inpatient stroke rehabilitation, highlights their significant role, identifies current barriers/challenges of implementing stroke nursing care, and suggests ways of documenting and measuring nurses’ contributions.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".