Supplementary Material for: Examination of the Healthy Caregiver Effect among Older Adults: Findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Introduction: The Healthy Caregiver Hypothesis (HCH) suggests that caregiving is associated with beneficial health impacts for family caregivers. However, mixed results have been reported, particularly when different levels of caregiving intensity were examined. This study analyzes the relationship between caregiving intensity and three health indicators (functional health, chronic illness, and self-rated general health) among Canadian older adults over 3 years. Methods: We drew upon a subsample of 11,344 participants aged 65 years and older from the Baseline and Follow-up 1 data of the Canadian Longitudinal Study on Aging and used linear mixed models to test the hypothesis based on different levels of caregiving intensity. Results: Older adults who provided low-intensity care recently or continuously reported better functional health and self-rated health than noncaregivers. In contrast, older adults with low-intensity caregiving responsibility reported more chronic conditions over time compared to noncaregivers, but this association was not found for high-intensity caregivers. Discussion/Conclusion: This study elucidates the HCH by incorporating caregiving intensity to understand patterns of better functional health and perceived health but more chronic conditions. The findings yielded from different health indicators suggest the impact of caregiving on health may be domain specific.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.612 | 0.085 |
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".