Long-term Care Trajectories in Canadian Context: Patterns and Predictors of Publicly Funded Care
Bibliographic record
Abstract
Objectives: Drawing on a structural life course perspective (LCP), we examined the most common trajectories experienced by older long-term care (LTC; home and community-based care, assisted living, and nursing home care) recipients. The overall sequencing of care transitions was considered along with the role of social structural location, social and economic resources, and health factors in influencing them. Method: Latent class and latent transition analyses were conducted using administrative data obtained over a 4-year period for clients aged 65 and older (n = 2,951) admitted into publicly funded LTC in 1 Canadian health region. Results: Four main LTC trajectories were identified within which a wider range of more specific or secondary subtrajectories were embedded. These were shaped by social structural factors (age, gender, rural-urban residence), social and economic resources (marital status, income, payment for services), and health factors (chronic conditions, functional and cognitive impairment and decline, problematic behaviors). Discussion: Our findings support the utility of a structural LCP for understanding LTC trajectories in later life. In doing so, they also reveal avenues for enhancing equitable access to care and the need for options that would increase continuity and minimize unnecessary, untimely, or undesirable transitions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".