From Assessment to Implementation: Innovations to Decreasing Dementia Risk in Rural Settings
Bibliographic record
Abstract
Abstract Though rural U.S. counties comprise 85% of the “older-age counties,” where more than 20% of residents are >65, innovative interventions to minimize the age-related risk of dementia are not well established. This symposium offers advances in assessment, recruitment, data collection, and interventions among rural U.S. regions. Fergen and colleagues share findings from their approach of using merged ethnographic and socioecological frameworks to identify dementia-focused themes that can be applied when developing interventions across the home, clinic, and community in rural northern Minnesota. Second, Cook et al. reveals new findings regarding the intersection of social isolation and environment in the rural community, through novel smartwatch-based measures of socialization and physical activity and how they relate to cognitive performance in older racially/ethnically diverse adults living in a rural southern Florida farmworker region. Third, Ghazi and colleagues compare the level of dementia risk as measured by the Montreal Cognitive Assessment (MoCA) across rural, micropolitan, and urban samples as a means for informing future innovations. Wiese’s team concludes the symposium by demonstrating the effectiveness of adapting a faith-based framework for cancer detection and diagnosis, to a new focus area; cognitive decline. This model, tested in a southern, rural, racially/ethnically diverse cohort, can serve as an upstream approach to increase primary (education), secondary (screening), and tertiary (treatment) of dementia in other rural settings. Finally, as discussant, Ford will synthesize implications of these findings for policy and practice. Combined, these abstracts provide a pathway for diminishing the threat of cognitive decline in rural communities.
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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".