LIFE COURSE TRAJECTORIES OF FAMILY CARE
Bibliographic record
Abstract
Objectives. This study identified lifetime patterns of caring trajectories among older Canadians. Most caregiving research often measures care as a point-in-time status while ignoring trajectories of care across the lifecourse, so we know little about persistence and timing of caregiving, or accumulation of associated risks. The few qualitative studies examining caring trajectories focus on the course of care recipients’ illnesses rather than caring pathways across caregivers’ lifecourses. Methods. Latent Class Analyses of a sub-sample from Canada’s nationally representative 2012 survey on Caregiving and Care Receiving (age 65+, n=3,259) identified caring trajectories based on patterns in the number, timing, duration, and overlap among care episodes. Multinomial regression identified sociodemographic factors explaining membership in care trajectory groups. Results. Five unique trajectories were identified: Later life onset (n=1,606; fewest episodes, latest onset, shortest duration, no overlap); Generational (n =869; more than one episode, late midlife onset, little overlap); Midlife Juggler (n=474; multiple episodes, early midlife onset, long duration, multiple overlaps); Serial (n=220; multiple episodes, early onset, moderate duration, little overlap); and Early onset career (n=120; multiple episodes, earliest onset, longest duration, multiple overlaps). Women were more likely to be Jugglers (p<.01) or Early onset carers (p<.01) than Later life onset carers. Discussion. For most, caregiving is a process of moving into/out of care episodes over the life course with considerable variability in the timing, number, and duration of these episodes which likely results in differential accumulation of impact over the life course.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".