Trajectories of family care over the lifecourse: evidence from Canada
Bibliographic record
Abstract
Abstract In the midst of a ‘care crisis’, attention has turned again to families who are viewed both as untapped care resources and as disappearing ones. Within this apparent policy/demographic impasse, we test empirically theorised trajectories of family care, creating evidence of diverse patterns of care across the lifecourse. The study sample, drawn from a Statistics Canada national survey of family care, comprised all Canadians aged 65 and older who had ever provided care (N = 3,299). Latent Profile Analysis yielded five distinct care trajectories: compressed generational, broad generational, intensive parent care, career care and serial care. They differed in age of first care experience, number of care episodes, total years of care and amount of overlap among episodes. Trajectories generally corresponded to previously hypothesised patterns but with additional characteristics that added to our understanding of diversity in lifecourse patterns of care. The five trajectories identified provide the basis for further understanding how time and events unfold in various ways across lifecourses of care. A gap remains in understanding how relationships with family and social network members evolve in the context of care. A challenge is presented to policy makers to temper a ‘families by stealth’ policy approach with one that supports family carers who are integral to health and social care systems.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".