Neighborhoods, cultural factors, and alcohol use disorder
Bibliographic record
Abstract
Purpose: Trends in heavy drinking among Hispanic young adults are shifting in the United States. Despite elevated risks for alcohol use disorder (AUD) and related problems among Hispanic populations, little work has examined cultural mechanisms to alcohol outcomes in the context of their neighborhood environments. This study examines how neighborhood environment factors (ethnic density and distance to the nearest border port of entry) and cultural factors (acculturative stress and ethnic identity) relate to AUD. Methods: The sample of 575 Mexican American men and women (aged 18 to 30) was recruited from San Diego County, California, USA. We assessed indirect effects of Mexican American ethnic density and distance to the nearest border port of entry on AUD through acculturative stress and ethnic identity. Multiple group path analysis was used to test sex differences. Results: While sex differences were not observed in the overall model, there were differential associations with focal variables for each group. For women, greater proportions of ethnic density and greater distance to the nearest port of entry both were negatively associated with AUD. For both women and men, acculturative stress was positively associated with AUD, and neighborhood environment indicators were not related to acculturative stress nor ethnic identity. Conclusions: These data may inform further studies to integrate social and cultural mechanisms in creation of place-based strategies for AUD prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".