Racial Disparities in Cognitive Functioning: The Mediating Role of Social Resources
Bibliographic record
Abstract
Abstract Despite frequent recognition of disparities in cognitive functioning between White and non-White older adults, the pathways or mechanisms through which race affects cognitive functioning have yet to be elucidated. The research questions addressed in this paper are: 1) Is there a relationship between racial minority status and cognitive functioning in middle and later life? 2) To what extent do social resources (i.e., social support, social networks, and social participation) mediate the relationship between racial minority status and cognitive functioning? 3) Finally, drawing on intersectionality theory, if social resources do mediate the relationship between racial minority status and cognitive functioning, to what extent is this mediation effect moderated by the interaction of gender and Socioeconomic Status (SES)? Using cross-sectional data drawn from the Canadian Longitudinal Study on Aging (CLSA) with a sample of over 50,000 Canadians (2010-15) aged 45 to 85 years, multivariate regression analyses (OLS, logistic, multinomial logistic) assess the mediating effect of social resources on the relationship between racial minority status and cognitive functioning. Controlling for age, gender and other relevant determinants, preliminary results reveal that racial disparities in cognitive functioning (i.e., lower cognitive test scores) exist in Canada and that this relationship is partially mediated by some indicators of social resources (e.g., functional social support, emotional social support). Our findings suggest the need for interventions targeted at increasing social resources for racial minority groups to cope with the risk of developing cognitive impairment in later life.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".