Advocating for Change: Lessons Learned from Ontario’s Quest for a Renewed Dementia Strategy
Bibliographic record
Abstract
Abstract Dementia has been labelled a public health priority by the WHO; with prevalence expected to triple over the next 30 years, urgent, whole‐of‐government action is needed to prepare for unprecedented strains on our health, home, and long‐term care systems. This session will focus on the experiences of Ontario, Canada’s largest province, detailing how government, private, and not‐for‐profit stakeholders can collaborate to influence policy. The effectiveness of policy networks as a tool to create policy change, specifically in the dementia space, will be examined using Ontario as an example. Learning outcomes will include a better understanding of how public institutions make decisions, what guides a policy agenda and how to influence such, how to create a sense of urgency, defining ageing as a problem worthy of public consideration, and evaluating the success of advocacy efforts. Participants will be encouraged to consider networks they can join and/or form in their respective jurisdictions, and how they can contribute personally and/or professionally to advocacy efforts. Examples of successful advocacy initiatives in Ontario will be examined. These include public campaigns leading up to and during the 2022 Ontario general election; the Roadmap Towards a Renewed Ontario Dementia Strategy ( www.alzheimer.ca/on/roadmap ); and the Ontario Dementia Care Alliance ( https://alzheimer.ca/on/en/take‐action/policy‐advocacy/ontario‐dementia‐care‐alliance ).
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 | 0.028 | 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 | 0.036 | 0.022 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".