Older Adults Caring for Older Adults: Physically Active Leisure Lifestyles as a Coping Resource for the Health of Caregivers
Bibliographic record
Abstract
There is growing evidence that caregiving workloads contribute to increased stress and decreased leisure participation. Given that physically active leisure can be an important coping resource for older adults, increases in stress and decreases in leisure may put the health of older caregivers in double jeopardy. A model of the impact of caregiver workload on physically active leisure, role-related stress, and physical and mental health, and the role of physically active leisure in ameliorating the negative effects of workload was developed and tested using structural equation modeling procedures. One hundred and fifty-two primary caregivers of individuals 60 years of age or over who were, themselves, 60 years of age or over, participated in a survey of lifestyle and caregiving experiences. Higher caregiving workloads were significantly linked to higher levels of role-related stress and poorer physical health, which in turn, were linked to lower levels of psychological well-being and higher levels of depression. However, having a physically active leisure lifestyle contributed to better physical health and less role-related stress mitigating to some extent the negative effects of workload. Physically active leisure did not appear to be directly affected by caregiver workload; however, other factors involved in living closer to care receivers appear to have a negative influence on frequency of participation in physically active leisure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".