Inequalities in Transitions to Home Care: A Longitudinal Analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: To investigate inequalities in transitions to home care across a broad set of demographic and socioeconomic factors in Canadian middle-aged and older adults. DESIGN: Longitudinal, retrospective cohort study. SETTING AND PARTICIPANTS: A total of 51,338 community-dwelling adults aged 45+ years, using national data from the Canadian Longitudinal Study on Aging across 3 timepoints from 2011 to 2021. METHODS: We analyzed transitions in home care use using multistate Markov models, with home care use and nonuse as transient states, and loss to follow-up as a terminal state. We calculated hazard ratios for transitions between states adjusting for factors related to home care need (ie, functional limitations, chronic conditions) within the following equity strata: income, education, immigration history, sex, gender, rurality, racial background, and tangible social support. RESULTS: Across all timepoints, 5.4% of non-home care users transitioned to home care by the next timepoint and 33.2% of home care users continued to use home care at the next timepoint. Among non-home care users, identifying as a woman, female, white, completing higher levels of education, having higher income, and having less support available was associated with an increased likelihood of transitioning to home care use. Among home care users, higher income was also associated with a greater likelihood to discontinue using home care compared with lower income users. The association between income and home care use was stronger among female individuals. CONCLUSIONS AND IMPLICATIONS: We found meaningful differences in home care transitions across several equity strata. Individuals with higher income have greater ability to access to private care, creating inequity in access to home care services. Gendered factors such as income and social support have important associations with home care use. Home care planning and policy must address the unique barriers and disadvantages diverse populations face to ensure equitable use of home care and promote healthy aging.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".