Differences in the Experiences and Support Needs of Family Caregivers to Stroke Survivors: Does Age Matter?
Bibliographic record
Abstract
PURPOSE: The rehabilitation stage of a stroke survivor's recovery provides an opportunity to prepare family caregivers for the supportive role they will play in the community. The goal of this qualitative study was to learn about family caregivers' experiences and support needs during the rehabilitation phase to inform program development. METHOD: We recruited family caregivers within the first 6 months post stroke. Nine caregivers participated in 40- to 60-minute in-depth qualitative interviews where the personal needs of caregivers were illuminated. Data were analyzed using content analysis. RESULTS: An overriding theme was differences in personal needs between older and younger caregivers. We interviewed five younger caregivers (55 years of age) and four older caregivers (>55 years of age). Younger caregivers identified informational support and training as important parts of their social support whereas older caregivers did not. Younger caregivers were also more likely to complain or criticize the health care system and staff than older caregivers. A common theme among older caregivers was to focus on the importance of keeping a positive outlook throughout the experience. CONCLUSION: Caregiver experiences and support needs varied according to age. This suggests that support programs should consider age as a factor when tailoring interventions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".