Caregiving and Union Instability in Middle and Later Life
Bibliographic record
Abstract
Objective: This study addressed the implications of informal caregiving for subsequent union instability in middle and later life, focusing on associations between gender and union type (marital, cohabiting) and the risk of separation or divorce within heterosexual unions. Background: Although caregiving is widely reported to be stressful and to have negative implications for the health and well‐being of individual caregivers, its implications for union stability are less clear. This is particularly evident with regard to middle‐aged and older adults in both marital and cohabiting unions. Yet caregiving may increase separation or divorce risk by operating as a stressor on the marital or union relationship, leading to lower union quality and subsequent dissolution. Method: Using retrospective data on union histories for a national population‐based cohort of married or cohabiting Canadian men and women aged 45 years and older ( N = 17,194), Cox proportional hazard models were used to examine the main and interactive effects of caregiving history, union type, and gender on union dissolution from age 45 to the time of the survey. Results: Caregiving was more destabilizing when it involved cohabiting rather than marital unions and female rather than male caregivers. Conclusion: These findings suggest that caregiving is associated with union dissolution but that the type of union and gender need to be considered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".