LONGER LIVES AND THE DETERMINANTS OF COGNITIVE HEALTH: RURAL OLDER ADULTS’ PERSPECTIVES
Bibliographic record
Abstract
In North America, life expectancy has rapidly increased with advances in healthy diet, physical activity, and education. However, increased life expectancy is strongly linked to cognitive health, and age is the greatest risk factor for developing dementia. There is a paucity of research on aging and cognitive health within a rural context. This presentation has two objectives: 1) to explore the key determinants of cognitive health from the perspective of rural seniors; and 2) to identify actions and interventions associated with supporting cognitive health. Guided by ethnographic methodology and a community-based approach, data were collected through semi-structured interviews with 42 seniors in rural Saskatchewan, Canada. Drawing on lay theory and cultural schema theory, data were compiled and coded using thematic analysis. In describing the determinants of cognitive health, a framework of four domains emerged including social health, intellectual health, emotional health, and functional health. In discussing interventions to support cognitive health, rural seniors highlighted the importance of one’s ability to communicate, participate in activities, function in day-to-day life, engage in intellectual stimulation, and support their emotional wellbeing. The lack of research on rural seniors’ perceptions of cognitive health has an important influence on the interventions designed to support rural aging. Exploring rural seniors’ perspectives provides valuable information to advance knowledge on the key factors, implications, and interventions to support rural cognitive health. In moving forward, collaboration with rural seniors is critical to the development of appropriate programs and strategies aimed at early dementia diagnosis, awareness, and education in rural settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".