CARE TRAJECTORIES AND WELLBEING OUTCOMES OF OLDER CARERS
Bibliographic record
Abstract
Abstract The UN Decade of Healthy Ageing states that family carers should not be held responsible for care. Yet families continue to provide the majority of care to members with long term health problems and disabilities. Their contributions to the economy remain unrecognized; their costs noted but relegated to private family matters. The purpose of this presentation is to determine wellbeing outcomes of older Canadians with diverse life course trajectories of care. Data are from the 2018 Statistics Canada nationally representative survey on caregiving. Sample for this study is ≈3000 people 65+ with one or more care episodes across their life course. Based on earlier conceptual and empirical work, we recreated 5 care trajectories. Wellbeing was measured based on material, relational and subjective domains. We conducted multivariate analyses (OLS, logistic and ordered logistic regressions) appropriate to the nature of the dependent variable) to assess whether care trajectory type predicted later life wellbeing. Results show significant differences in wellbeing among trajectory types. Serial carers had longest years of care and most care episodes. They experienced the most negative wellbeing outcomes compared to carers with other trajectory types in material wellbeing (poorer physical and mental health, lower employment income), and subjective wellbeing (higher stress). There were no significant differences across care trajectory types in relational wellbeing (loneliness). We discuss the place of public policy in addressing patterns of cumulative disadvantage in life courses of family care; and call for the development of indicators of wellbeing domains that best reflect these outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".