Building community capacity for dementia in Canada: new directions in new places
Bibliographic record
Abstract
Building Capacity for Meaningful Participation by People Living with Dementia is being undertaken in Canada under the umbrella of the federally funded Dementia Community Investment. This four-year Building Capacity Project (2019–23) models a bottom-up cross-sectoral approach to building and connecting community-based activities that provide meaningful opportunities for people with dementia to remain active and socially connected. In this chapter we examine how the project is designed to build practical knowledge from the ‘ground up’, working closely with people who are most directly affected by the issues (especially those with lived experience of dementia), and taking local context into account using methods of asset-based community development and developmental evaluation. Most importantly, this work is being carried out in two very different places, which is allowing us to both leverage our unique strengths and discover the common principles underlying successful approaches that can then be scaled up more broadly, including nationally. Ultimately, the aim is to create and share new knowledge about how this kind of ‘grass roots’ approach can lead to sustainable change at individual, community and institutional levels to promote social inclusion, raise awareness and reduce stigma around dementia. Recognising the importance of place in this project, we begin by describing where the work is happening. This allows us to make explicit the significance of local contexts, be they geographic, social, economic, cultural or political, which are intrinsic to how we are developing and evaluating the various activities and programmes.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.024 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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".