“Caregiving is a full‐time job” impacting stroke caregivers' health and well‐being: A qualitative meta‐synthesis
Bibliographic record
Abstract
Family caregivers contribute to the sustainability of healthcare systems. Stroke is a leading cause of adult disability and many people with stroke rely on caregiver support to return home and remain in the community. Research has demonstrated the importance of caregivers, but suggests that caregiving can have adverse consequences. Despite the body of qualitative stroke literature, there is little clarity about how to incorporate these findings into clinical practice. This review aimed to characterise stroke caregivers' experiences and the impact of these experiences on their health and well-being. We conducted a qualitative meta-synthesis. Four electronic databases were searched to identify original qualitative research examining stroke caregivers' experiences. In total, 4,481 citations were found, with 39 studies remaining after removing duplicates and applying inclusion and exclusions criteria. Articles were appraised for quality using the Critical Appraisal Skills Programme (CASP), coded using NVivo software, and analysed through thematic synthesis. One overarching theme, 'caregiving is a full-time job' was identified, encompassing four sub-themes: (a) restructured life, (b) altered relationships, (c) physical challenges, and (d) psychosocial challenges. Community and institution-based clinicians should be aware of the physical and psychosocial consequences of caregiving and provide appropriate supports, such as education and respite, to optimise caregiver health and well-being. Future research may build upon this study to identify caregivers in most need of support and the types of support needed across a broad range of health conditions.
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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 | 0.065 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".