Social determinants of health and cognitive performance of older adults living in rural communities: The Three Villages Study
Bibliographic record
Abstract
OBJECTIVES: There is limited information on factors associated with poor cognitive performance in rural settings of Low- and Middle-Income Countries. Using the Three Villages Study Cohort, we assessed whether social determinants of health (SDH) play a role in cognitive performance among older adults living in rural Ecuador. METHODS: Atahualpa, El Tambo and Prosperidad residents aged ≥60 years received measurement of SDH by means of the Gijon Scale together with a Montreal Cognitive Assessment (MoCA). The association between SDH and cognitive performance (dependent variable) was assessed by generalized linear models, adjusted for demographics, years of education, cardiovascular risk factors, symptoms of depression and biomarkers of structural brain damage. RESULTS: We included 513 individuals (mean age: 67.9 ± 7.3 years; 58% women). The mean score on the Gijon scale was 9.9 ± 2.9 points, with 237 subjects classified as having a high social risk (≥10 points). The mean MoCA score was 19.6 ± 5.4 points. Locally weighted scatterplot smoothing showed an inverse linear relationship between SDH and MoCA scores. SDH and MoCA scores were inversely associated in linear models adjusted for clinical covariates (β: -0.17; 95% C.I.: -0.32 to -0.02; p = 0.020), neuroimaging covariates (β: -0.17; 95% C.I.: -0.31 to -0.03; p = 0.018), as well as in the most parsimonious model (β: -0.16; 95% C.I.: -1.30 to -0.02; p = 0.026). CONCLUSIONS: Study results provide robust evidence of an inverse association between SDH and cognitive performance. Interventions and programs aimed to reduce disparities in the social risk of older adults living in underserved rural populations may improve cognitive performance in these individuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".