FAMILY CAREGIVER HEALTH IN CANADA AND THE UNITED STATES: ARE THERE DIFFERENCES IN THE STRESS PROCESS?
Bibliographic record
Abstract
Family caregivers provide the majority of care to older adults as well as others with chronic illnesses and disabilities in virtually all countries. Despite some positive aspects of caregiving, research suggests that caregiving is often stressful, and has negative implications for caregiver health. These implications tend to be conceptualized using a stress process model that views individual social background factors, primary and secondary stressors, and stress mediators as influencing caregiver health. Yet, whether and how these factors impact caregiver health across national contexts with different populations and health care policies remains unclear. This study addressed this gap using data on family caregivers from Canada and the United States drawn from the 2012 Canadian General Social Survey (n=6,985) and the 2015 AARP Survey on Caregiving (n= 1,091). Ordered logit and logistic regression models addressed the predictors of self-reported health and worsening health status as a result of caregiving in each country. Results revealed that background factors (age, education, gender) operated similarly in their impact on the self-reported health of caregivers in both countries but varied in their impact on perceived declines in health status as a result of caregiving. Primary and secondary stressors operated similarly in both countries on both health outcomes. However, whereas coping operated similar in both countries, other stress-mediators including informal and formal social support had differing impacts in the two countries on both health outcomes. The implications of the findings for our understanding of the stress process and for interventions to improve family caregiver health are discussed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".