The Municipal Role in Long-Term Care
Bibliographic record
Abstract
By the mid-2030s, approximately 1 in 4 Canadians could be over the age of 65. This demographic shift, combined with the acute crisis brought on by the COVID-19 pandemic, has made reforming long-term and seniors’ care an urgent issue. In general, responsibility for providing care to seniors falls to provinces, which in turn benefit from significant federal transfers to help fund services in this area. In Ontario, however, municipalities share in the delivery of seniors’ care, and are required to run a minimum number of long-term care homes. Moreover, their responsibilities in urban planning extend to designing age-friendly communities that meet the needs of older populations. The seventh report in the Who Does What series from the Institute on Municipal Finance and Governance (IMFG) and the Urban Policy Lab examines the role that municipalities play in long-term and elder care, with a special focus on Ontario municipalities. Pat Armstrong argues that municipal long-term care facilities provide the best care and working conditions relative to private and for-profit homes. She calls for Ontario to build upon its role with respect to funding and regulating municipal long-term care homes by improving wages for workers in these facilities. She also suggests that the federal government apply conditions to transfer payments to encourage other orders of government to adopt higher standards of care. Daniella Balasal and Nadia De Santi discuss the concept of age-friendly communities, describing how municipalities are developing strategies and plans to meet the needs of their aging populations outside institutional settings. They cite Ontario’s age-friendly community planning guide as an overarching framework for municipalities to develop local strategies and plans. Shirley Hoy advocates for a foundational restructuring of the long-term care sector. Hoy calls for deep integration of provincial health services, such as doctors and hospitals, with the broader elder care system. Given their role in providing both long-term care and social services, municipalities have a critical part to play in coordinating primary care, long-term care, and community-based supports. Hoy adds that the 2023 health care funding deal between the federal government and the provinces could act as the impetus to strengthen long-term care at the local level.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".