DOES DEMENTIA POLICY ACCOUNT FOR DIVERSITY? AN ANALYSIS OF CANADIAN DEMENTIA POLICIES
Bibliographic record
Abstract
Abstract Background: The World Report on Ageing and Health outlines key policy challenges that need to be addressed to have successful public health response to population aging, including ‘dealing with diversity’ and ‘reducing inequity.’ Dementia has been framed as a global health challenge affecting approximately 46 million people worldwide. To reduce inequality, dementia policies must account for and respond to diversity. The purpose of this research was to conduct an analysis of current Canadian federal, provincial, and territorial dementia strategies to examine their inclusion of dimensions of diversity. Method: We conducted an internet-based search and identified 13 unique Canadian federal, provincial, and territorial dementia documents. We completed a deductive content analysis to review each policy for content on: age and sex; racial and ethnic identity; sociocultural identity; religion; socioeconomic status; gender identity and sexual orientation; geographical location; and language fluency and communicative ability. Results: Within Canadian dementia policies there is minimal focus on diversity. When diverse identities were acknowledged in policies, very little guidance was provided to local policy-makers, healthcare administrators, or service providers in how to acknowledge and accommodate different group’ needs with services. Further, none of the policies adopted an intersectional approach; that is, they failed to recognize that older adults have several overlapping and interrelating identities. Conclusion: As Canada and other countries move towards developing and revising dementia policies it is imperative that they account for diversity within aging populations.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.036 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".