Neighborhood Social Capital, Neighborhood Attachment, and Dental Care Use for Los Angeles Family and Neighborhood Survey Adults
Bibliographic record
Abstract
OBJECTIVES: We tested the hypothesis that neighborhood-level social capital and individual-level neighborhood attachment are positively associated with adult dental care use. METHODS: We analyzed data from the 2000-2001 Los Angeles Family and Neighborhood Survey that were linked to US Census Bureau data from 2000 (n = 1800 adults aged 18-64 years across 65 neighborhoods). We used 2-level hierarchical logistic regression models to estimate the odds of dental use associated with each of 4 forms of social capital and neighborhood attachment. RESULTS: After adjusting for confounders, the odds of dental use were significantly associated with only 1 form of social capital: social support (adjusted odds ratio [AOR] = 0.85; 95% confidence interval [CI] = 0.72, 0.99). Individual-level neighborhood attachment was positively associated with dental care use (AOR = 1.05; 95% CI = 1.01, 1.10). CONCLUSIONS: Contrary to our hypothesis, adults in neighborhoods with higher levels of social capital, particularly social support, were significantly less likely to use dental care. Future research should identify the oral health-related attitudes, beliefs, norms, and practices in neighborhoods and other behavioral and cultural factors that moderate and mediate the relationship between social capital and dental care use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".