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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".