What Makes Family Caregivers Happy During the First 2 Years Post Stroke?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study aimed to identify aspects of the caregiving situation contributing to family caregivers' psychological well-being. METHODS: Longitudinal cohort study with structured quantitative interviews 1, 3, 6, and 12 months post stroke. A subset of participants also completed surveys 18 and 24 months post stroke. Participants included individuals hospitalized for their first stroke and their family caregivers. Psychological well-being was assessed by the Positive Affect Scale. RESULTS: A total of 399 stroke survivor, caregiver dyads completed the 1-year follow-up and 80 dyads completed the second year of follow-up. Using mixed effects modeling for longitudinal data, caregivers reported more psychological well-being when they provided more assistance to stroke survivors who had fewer symptoms of depression, better cognitive functioning, and who had more severe strokes. In addition, caregivers who maintained participation in valued activities had more mastery, gained personally providing care, were in better physical health, were older, and were from Quebec reported more psychological well-being. Caregivers followed for a second year post stroke reported better psychological well-being when caring for stroke survivors with fewer symptoms of depression and more severe strokes and when the caregivers had a greater sense of mastery and gained more personally providing care. CONCLUSIONS: Our findings contribute to the caregiver intervention development literature by identifying aspects of the caregiving situation that are associated with positive outcomes. Incorporating specific aspects, for example, strategies to enhance caregiver mastery into programs and services offered to caregivers may enhance their positive experiences with providing care and ultimately enhance the sustainability of the caregiving situation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".