Impact of caring for individuals with stroke on perceived physical health of informal caregivers
Bibliographic record
Abstract
PURPOSE: Caring for a person who has experienced stroke can be a formidable task placing considerable demands upon the informal caregiver. Although the impact of caring on mental health has been well established, less convincing associations between caregiving and declining physical health have been reported. In order to examine the impact of caregiving over time on the physical health of individuals providing informal care for individuals with stroke, we conducted a review of the published literature. METHOD: Literature searches of three electronic databases were conducted for the years 1996-2008 to identify studies providing longitudinal data based on repeated quantitative assessment of physical health. Pooled analyses were conducted. RESULTS: Sixteen studies were identified for inclusion. On the basis of pooled analyses, perceived physical health appeared stable over time. When comparisons to normative values were possible, the experience of health did not differ substantially from age- and sex-matched norms. Associations between physical health and psychological distress or well-being were identified. CONCLUSIONS: Informal caregiving does not result, necessarily, in reduced physical health. Further study is required to examine the timing and nature of the relationship between psychological distress and physical health to inform provision of services intended to maintain caregiver health and well being.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".