Paying for home care: a cross-sectional study of participants of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Home care supports older adults living in the community by providing medical, rehabilitative, and personal care at home. Across Canada there is wide variability in public funding models for home care, which may also be paid for privately. Our objective was to compare individual characteristics across different home care payment groups and examine associations between sociodemographic factors, health status, and private payment for care. METHODS: We included formal home care users from the Canadian Longitudinal Study on Aging (CLSA) between 2015 and 2021 and classified them into three groups based on how much of their home care was paid for out-of-pocket: none, part, or all. We used descriptive statistics to compare the individual and home care characteristics of the three groups. We used unadjusted and adjusted multinomial logistic regression models to examine associations with the home care payment groups. RESULTS: Of 44,817 participants in the CLSA, 3,580 were formal home care users. Using weighted proportions, 6.8% of the CLSA were home care users, and of these 46.2% reported paying nothing out-of-pocket, 12.7% paid partially, and 41.0% paid all costs. Individuals who paid all costs reported the best health, whereas those who paid partially reported the worst. Meal preparation/homemaking and housework/maintenance services were more commonly paid for privately, while medical care was more likely to be publicly funded. Higher-income individuals were more likely to pay entirely out-of-pocket and large provincial variations were noted across payment groups. CONCLUSIONS: Private home care is common in Canada, particularly for non-medical services. Income-related disparities may limit access for those unable to pay, contributing to inequities in aging. Policies ensuring equitable access to essential services will be critical as demand for home care grows.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".