Considering inequities in national dementia strategies: breadth, depth, and scope
Bibliographic record
Abstract
BACKGROUND: Considering that dementia is an international public health priority, several countries have developed national dementia strategies outlining initiatives to address challenges posed by the disease. These strategies aim to improve the care, support, and resources available to meet the needs of persons living with dementia and their care partners and communities. Despite the known impact of social determinants of health on dementia risk, care, and outcomes, it is unclear whether dementia strategies adequately address related inequities. This study aimed to describe whether and how national dementia strategies considered inequities associated with social determinants of health. METHODS: We conducted an environmental scan of the national dementia strategies of countries that are part of the Organisation for Economic Cooperation and Development (OECD). Included strategies had to be accessible in English or French. Sub-national or provincial plans were excluded. We synthesised information on strategies' considerations of inequity through a thematic analysis. RESULTS: Of the 15 dementia strategies that met inclusion criteria, 13 mentioned at least one inequity (M = 2.4, median = 2, range:0-7) related to Race/Ethnicity; Religion; Age; Disability; Sexual Orientation/Gender Identity; Social Class; or Rurality. Age and disability were mentioned most frequently, and religion most infrequently. Eleven strategies included general inequity-focused objectives, while only 5 had specific inequity-focused objectives in the form of tangible percentage changes, deadlines, or allocated budgets for achieving equity-related goals outlined in their strategies. CONCLUSIONS: Understanding if and how countries consider inequities in their dementia strategies enables the development of future strategies that adequately target inequities of concern. While most of the strategies mentioned inequities, few included tangible objectives to reduce them. Countries must not only consider inequities at a surface-level; rather, they must put forth actionable objectives that intend to lessen the impact of inequities in the care of all persons living with dementia.
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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 | 0.039 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".