Without Empowered Patients, Caregivers and Providers, a Community-Based Dementia Care Strategy Will Remain Just That
Bibliographic record
Abstract
In trying to cope with the needs of the growing number of people living with dementia (PLWD), jurisdictions around the world have been implementing a variety of strategies, policies and programs to enable better access to the supports they and those who care for them require. Despite considerable efforts that have been undertaken, PLWD and their caregivers still face considerable challenges in pursuing care pathways and community-based supports that can help them avoid premature institutionalization. Morton-Chang et al. (2016) have comprehensively reviewed jurisdictional approaches towards the development of dementia strategies, policies and programs; there is a growing understanding and consensus around the things we need to do as societies to better meet the needs of PLWD and their caregivers; however, progress to date could be best characterized as top-down, patchy and fragmented. This paper builds on Morton-Chang et al.'s (2016) assertion that the development of a comprehensive person and caregiver-centred community-based dementia strategy in Ontario and other parts of Canada is likely achievable, particularly if implemented using a "ground-up" approach that is well-aligned with other government-related initiatives.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.048 | 0.066 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".