Actual and ideal services in acute care and rehabilitation for relatives post-stroke from three perspectives: Relatives, stroke clients and health professionals
Bibliographic record
Abstract
OBJECTIVE: To document the gap between actual and desired ideal services for relatives post-stroke from three perspectives: relatives, stroke clients and health professionals. METHODS: A two-phase qualitative design and consisting of in-depth interviews (Phase 1) and 3 focus groups (Phase 2). The interview sample consisted of 25 relatives (mean age 53.4 (standard deviation 12.7); women = 21/25) and 16 individuals with a first stroke (mean age = 55.7 (standard deviation 11.2); women = 7/16). The focus group sample size varied from 5 to 7 participants. An interview guide validated by experts was used. Audio content was transcribed verbatim and rigorously analyzed by two team members. RESULTS: Services received by relatives are diversified, and relatives' perceptions range from receiving no services to being satisfied with services received. Even when participants were satisfied, ideal services were still desired: they would have liked to receive services earlier and without having to seek. Four main factors emerged as influencing the amount and quality of services received, including the individual's ability to seek. CONCLUSIONS: A gap remains between actual and ideal services for relatives post-stroke. It is crucial to legitimized relatives' role as clients and to systematically assess the patient's social environment in order to provide services in accordance with needs.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".