WHO NEEDS THEIR NEIGHBORS? EXPLORING NEIGHBORHOOD DISPARITIES IN COGNITIVE FUNCTION THROUGH PATH ANALYSIS
Bibliographic record
Abstract
Abstract Neighborhoods are diverse and may or may not present opportunities for stress reduction or social engagement depending on their qualities. Based on the stress connectome (Dum et al., 2019), psychosocial stress may accelerate cognitive aging, which explains place-based disparities in cognitive function. This study examines two attributes of the neighbourhood environment, and their potential to influence cognitive function through semantic fluency.Using aggregated baseline (cross-sectional) data from N=1,010 neighborhoods with 5 or more respondents in the Canadian Longitudinal Study on Aging, we examined the effects of neighborhood greenness and cohesion on aggregates of age-, sex-, and education-adjusted cognitive test scores. Participants were community-dwelling adults aged 44 and above. Semantic fluency was assessed using the Animal Fluency Test (AFT). Delayed recall was assessed using Rey’s Auditory Verbal Learning Test (RAVLT), whereas executive function was assessed using Mental Alternation Test (MAT). Neighborhood qualities were found to affect delayed recall (B=.34, p<.001) and executive function (B=.42, p<.001) through semantic fluency (B=.08, .10, p<.01). Semantic fluency fully mediated the effects of neighborhood attributes on cognitive function. Further stratifying these neighborhoods by socioeconomic status showed that cohesion has stronger effects in poorer neighborhoods (B indirect=.104) than richer neighborhoods (B indirect=.066). The effect of greenness was no longer significant upon stratification. Neighborhoods offer an important social arena for adults in mid- and late-life to practice conversing, especially in poorer neighborhoods, which improves cognitive function. Creating opportunities for socialization by improving cohesion and neighborhood parks may reduce place-based disparities in cognitive health. Causality remains to be ascertained.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".