B - 44 Social Capital and Aging across Cultures: Examining the Mediating Effects on the Cognitive Functioning-Functional Ability Relationship in Baltimore and Mumbai
Bibliographic record
Abstract
Abstract Objective Examine social capital (social support, social network) as a mediator of the cognitive functioning and functional ability relationship in older adult populations. Examine this relationship cross-culturally, with culture as a moderator of the mediation model. Method 75 community dwelling participants recruited across India and the United States. Cognitive function was measured using the Montreal Cognitive Assessment. Functional ability was evaluated using the Duke OARS Multidimensional Functional Abilities Questionnaire and the World Health Organization Disability Assessment Schedule (WHODAS). Social network and social support were evaluated using the Lubben Social Network Scale and Multidimensional Scale of Perceived Social Support. Process Macro (Hayes & Rockwood, 2017) was used to examine the mediation and moderated mediation models. Results The internal consistency of the MoCA and OARS were below acceptable ranges in the Indian sample. Indian participants reported more social support than U.S participants, however, there were no differences in social network. Social support predicted functional ability as measured by the WHODAS, F(1, 63) = 4.34, p = 0.04, but not the OARS. Social network predicted functional ability as measured by both the WHODAS, F(1, 57) = 8.33, p = 0.006, and the OARS, F(1, 62) = 4.98, p = 0.03. Results suggested that social capital was not a significant mediator of the relationship, and that culture was not a significant moderator. Conclusions Limitations due to small sample size indicate need for further exploration of the potential benefits of social capital on cognitive and functional ability across cultures. Important to consider appropriateness of measures when working with non-normative groups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".