Home Care Utilization Patterns Among Canadians with Dementia Living in Rural and Urban Settings: A Cross-Sectional Study
Bibliographic record
Abstract
Context: Home care is a cost-effective solution for patients with dementia. There is an important need to strengthen formal home care systems to meet the increasing prevalence of dementia in Canada. Objective: This study aims to describe the home care utilization patterns of Canadians aged 50 and older with dementia compared to those without. This study further seeks to understand the impact of geography (rural, small population centre (PC), medium PC, and large PC) on home care utilization among Canadians with dementia. Study design and analysis: This study used a cross-sectional design. Variables were selected using the Behavioural Model of Health Services Use. Canadians aged 50 and older were grouped by dementia status, age, sex, and rurality. Groups were compared using unadjusted odds ratios (ORs) with 95% confidence intervals (CIs). Dataset: Data from the 2021 Canadian Community Health Survey (CCHS) was used for this study. Population studied: Canadians aged 50 years and older who responded to the 2021 CCHS. Intervention/Instrument: N/A Outcome Measures: Home care utilization in the past 12 months. Results: Respondents of the 2021 CCHS represented 14,307,700 Canadians aged 50 and older. Of these, 1.31% (n=187,500) reported having dementia, and 98.69% (n=14,120,200) did not. Overall, respondents with dementia were more likely to use home care than those without. Among those with dementia, compared to rural respondents, respondents in small PCs had lower odds of using nursing care (OR=0.45, 95% CI 0.43-0.47), personal care (OR=0.74, 95% CI 0.70-0.77), medical equipment (OR=0.26, 95% CI 0.24-0.28), other health care (OR=0.03, 95% CI 0.02-0.03), and other services (OR=0.46, 95% CI 0.43-0.49). Similarly, respondents in medium and large PCs had lower odds of using most home care services. However, respondents in medium PCs had higher odds of using other services (OR=1.24, 95% CI 1.05-1.20), and respondents in large PCs had higher odds of using personal support (OR=1.46, 95% CI 1.41-1.50) than respondents in rural regions. Conclusion: Canadians with dementia are more likely to use home care services than those without. However, patients living in PCs access most of these services less than those in rural areas. Findings further suggest that small PC patients are the most underserved and access all types of home care services less than those in rural areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".