Comparative Analysis of Dementia Care Programs and Delivery Models
Bibliographic record
Abstract
Abstract Dementia has significant social and economic impacts for those living with dementia and their caregivers. Despite an increase in prevalence of complex chronic conditions and dementia, long-term care services are 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 programs and service delivery models across five different North American jurisdictions in Canada and the U.S. are compared using a deductive analytical approach using a comparative policy framework developed by Richard Rose. The policy efforts included in this research attempt to improve health system flow and access for vulnerable populations. One common theme among all jurisdictions are long-standing institutional barriers that can make change difficult. These barriers can prevent the ability for systems to be flexible and adapt to meet the changing needs of populations. Incrementalism is often considered an appropriate approach to health system reform. Yet, incremental change efforts lead to policy layers and these layers can lead to tension between different policy mixes and unintended consequences. These programs were introduced in a manner that did not fully consider how to patch current structures and risk creating further system redundancies. One approach to reduce this risk is to combine evaluative efforts that assess ‘goodness of fit’. The degree to which these programs have embedded these efforts successfully is low, with the possible exception of DSRIP from NY.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".