Considerations regarding Incorporating a Cash-for-care Program in Ontario's Approach to Care for Older Adults
Bibliographic record
Abstract
Ontario, like Canada more generally, has an aging population, which will exert further pressures on the approaches to providing care to older persons. Certain of these pressures are outlined, with the aid of population projections. Many developed countries, most of which have aging populations, have adopted various approaches to care provision for older adults, which differ from Ontario’s approach in certain ways. Ariaans et al. (2021) developed a typology based on the approaches used in 25 OECD countries but did not include Canada or Ontario in the analysis. This thesis analyzes the care approach used in Ontario along the dimensions developed by Ariaans et al. (2021) to place it within the typology used by Ariaans et al. (2021). A measure used by Ariaans et al. (2021) is whether a cash-for-care program is included. Ontario’s approach does not incorporate a cash-for-care program, whereas some other countries’ approaches do include a cash-for-care program. A scoping review was performed to identify and report on the benefits and disbenefits of a cash-for-care program, identified in the literature, and five themes were revealed. A form of framework analysis was used for more detailed exploration of the gender engraining aspects of cash-for-care programs. The discussion has special relevance to any proposed intervention, such as introduction of a cash-for-care program, because women play a disproportionately large role as carers, both paid and unpaid, and as care recipients in long-term care homes, and may be adversely affected.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".