Inequities in dementia risk and recommandations for inequity stratifiers collection in research
Bibliographic record
Abstract
Abstract Certain communities, including ethnic and cultural minorities, experience disproportionately higher rates of dementia and worse outcomes. 1,2 Structural and social determinants of health (SSDH)—the conditions in which people are born, live, work, and age 3 —are likely key drivers of these disparities. 4 The effects of SSDH accumulate over the lifecourse, influencing physical, mental, and brain health, as well as the ability to adopt healthy behaviors. 4 Despite growing recognition of their role in dementia inequities, SSDH remain inconsistently integrated into research, and Canada lacks specific guidelines for their inclusion in dementia studies. This project seeks to provide clear guidance for integrating SSDH into aging and dementia research in Canada. In collaboration with a network of stakeholders and knowledge users, we are developing a recommendation framework grounded in community and expert input. To ensure a comprehensive approach, we are 1) Engaging community partners (e.g., individuals with lived experience, advocates for marginalized communities) through mind mapping sessions to identify the factors they view as most critical to maintaining brain health in aging. 2) Conducting a Delphi survey that invites both field experts and community partners to prioritize key SSDH indicators. 3) Reviewing current practices for assessing SSDH in Canadian longitudinal studies on dementia and aging through a scoping review, the findings of which will further inform the recommendations. The resulting guidance framework will be shared as an online toolkit to facilitate its adoption and promote harmonization across Canadian cohorts. To support implementation, workshops and webinars will be organized to help researchers integrate SSDH into all stages of their work, from data collection to analysis and interpretation. Adopting a harmonized SSDH battery across Canadian cohorts will enable the generation of large, pooled datasets, allowing for robust investigations of SSDH variables and their interactions. Although this initiative is tailored to the Canadian context, we hope it can serve as a model for other countries and be adapted to address dementia‐related inequities worldwide. References 1. Lee et al. JAMA Network Open . 2022;5. 2. Mukadam et al. Int. J. Geriatr. Psychiatry . 2011;26. 3. Gómez et al. JPHMP . 2021;27. 4. Adkins‐Jackson et al. A&D . 2023;19.
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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 | 0.733 | 0.799 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.010 | 0.037 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".