Towards a Community-Based Dementia Care Strategy: How Do We Get There from Here?
Bibliographic record
Abstract
As recent policy reports in Ontario and elsewhere have emphasized, most older persons would prefer to age at home. This desire does not diminish for the growing numbers of persons living with dementia (PLWD). Nevertheless, many PLWD end up in residential long-term care (LTC) or in hospital beds. While LTC is valuable for PLWD with highly progressed cognitive and functional impairment requiring high-intensity care, it can be a costly and avoidable option for those who could remain at home if given early access to a coordinated mix of community-based supports. In this lead paper, we begin by exploring the "state of the art" in community-based care for PLWD, highlighting the importance of early and ongoing intervention. We then offer a brief history of dementia care policy in Ontario as an illustrative case study of the challenges faced by policy makers in all jurisdictions as they aim to re-direct healthcare systems focused on "after-the-fact" curative care towards "before-the-fact" prevention and maintenance in the community. Drawing on results from a "balance of care" study, which we conducted in South West Ontario, we examine how, in the absence of viable community-based care options, PLWD can quickly "default" to institutional care. In the final section, we draw from national and international experience to identify the following three key strategic pillars to guide action towards a community-based dementia care strategy: engage PLWD to the extent possible in decisions around their own care; acknowledge and support informal caregivers in their pivotal roles supporting PLWD and consequently the formal care; and enable "ground-up" change through policies and funding mechanisms designed to ensure early intervention across a continuum of care with the aim of maintaining PLWD and their caregivers as independently as possible, for as long as possible, "closer to home."
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".