Gender differences in home care clients and admission to long-term care in Ontario, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Home care is integral to enabling older adults to delay or avoid long-term care (LTC) admission. To date, there is little population-based data about gender differences in home care users and their subsequent outcomes. Our objectives were to quantify differences between women and men who used home care in Ontario, Canada and to determine if there were subsequent differences in LTC admission. METHODS: This is a population-based retrospective cohort study. We identified all adults aged 76+ years living in Ontario and receiving home care on April 1, 2007 (baseline). Using the Resident Assessment Instrument - Home Care (RAI-HC) linked to other databases, we characterized the cohort by living condition, health and functioning, and identified all acute care and LTC use in the year following baseline. RESULTS: The cohort consisted of 51,201 women and 20,102 men. Women were older, more likely to live alone, and more likely to rely on a child or child-in-law for caregiver support. Men most frequently identified a spouse as caregiver and their caregivers reported distress twice as often as women's caregivers. Men had higher rates of most chronic conditions and were more likely to experience impairment. Men were more likely to be admitted to hospital, to have longer stays in hospital, and to be admitted to LTC. CONCLUSIONS: Understanding who uses home care and why is critical to ensuring that these programs effectively reduce LTC use. We found that women outnumbered men but that men presented with higher levels of need. This detailed gender analysis highlights how needs differ between older women, men, and their respective caregivers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".