Policy programs and service delivery models for older adults and their caregivers: Comparing three provinces and two states
Bibliographic record
Abstract
Despite an increase in prevalence of complex chronic conditions and dementia, long-term care services are being continuously pushed out of institutional settings and into the home and community. The majority of people living with dementia in Canada and the United States (U.S.) live at home with support provided by family, friends or other unpaid caregivers. Ten dementia care policy programs and service delivery models across five different North American jurisdictions in Canada and the U.S. are compared deductively using a comparative policy framework originally developed by Richard Rose. One aim of this research was to understand how different jurisdictions have worked to reduce the fragmentation of dementia care. Another aim is to assess, relying on the theory of smart policy layering, the extent to which these policy efforts 'patch' health system structures or add to system redundancies. We find that these programs were introduced in a manner that did not fully consider how to patch current programs and services and thus risk creating further system redundancies. The implementation of these policy programs may have led to policy layers, and potentially to tension among different policies and unintended consequences. One approach to reducing these negative impacts is to implement evaluative efforts that assess 'goodness of fit'. The degree to which these programs have embedded these efforts into an existing policy infrastructure successfully is low, with the possible exception of one program in NY.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".