Postdischarge nursing interventions for stroke survivors and their families
Bibliographic record
Abstract
BACKGROUND: The physical, cognitive, and emotional sequelae of stroke underscore the need for nursing interventions across the continuum of care. Although there are several published studies evaluating community interventions for stroke survivors, the nursing role has not been clearly articulated. AIM: The aim of this paper is to report a study to describe, using a standardized classification system, the nursing interventions used with stroke survivors during the initial 6 weeks following discharge home. METHODS: In the context of a randomized controlled trial, two nurse case managers provided care to 90 community-dwelling stroke survivors who were assigned to the intervention arm of the trial. The nursing documentation was analysed, using the Nursing Intervention Classification (NIC) system, to identify and quantify the interventions that were provided. FINDINGS: Stroke survivors received, on average, six different interventions. There was a trend for those who were older, more impaired, and who lived alone to receive more interventions. The most commonly reported interventions included those directed towards ensuring continuity of care between acute and community care, family care, and modifying stroke risk factors. The study was limited to the nursing documentation, which may represent an underestimation of the care delivered. CONCLUSIONS: The NIC system was useful in capturing the interventions delivered by the nurse case managers. Nursing interventions are often not clearly articulated and less often use standardized terminology. Describing nursing activities in a standard manner will contribute to an increase in nursing knowledge and to evidence-based practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".