A roadmap for conducting more inclusive brain resilience research on aging and dementia
Bibliographic record
Abstract
Observed variabilities in cognitive and brain aging trajectories may be due to individualized and community-level differences in resilience. This in turn likely interacts with an individual’s biological sex, and sociocultural gender. However, there remains no clear guidance on how to best integrate diversity-related factors, in clinical and cognitive neuroscience research on resilience in aging and dementia. The international Brain Resilience and Diversity in Aging and Dementia (BReDAD) Collaboratory was established in 2024 with the goals of synthesizing knowledge, identifying knowledge gaps, and developing recommendations for conducting more inclusive research on resilience in aging and dementia. A focused review of the literature, including discussions and recommendations of the Collaboratory, leads to a roadmap for integrating diversity in future resilience research that includes: i) developing trust and meaningful long-term relationships with communities historically excluded from research, ii) diversifying who is engaged in all aspects of the research process, iii) adapting life course perspectives, iv) improving and expanding research designs and measurement tools, and v) using sensitive computational analytics and mixed methods for testing complex, intersectional, models. We conclude by recommending a transdisciplinary approach in resilience research to better reflect the complexities inherent in studying diversity and developing precision medicine outcomes.
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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 | 0.257 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.015 | 0.040 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.015 | 0.028 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".