Including drinking motives in multivariate models of acculturation and drinking behaviors among U.S. Hispanic college students: Does the story stay the same?
Bibliographic record
Abstract
OBJECTIVE: Previous research with U.S. Hispanic college students tends to find that higher levels of acculturation are associated with elevated alcohol use and that these effects are likely to be stronger for women than men. It is now important to consider the extent to which these associations remain once theoretically proximal predictors of alcohol use (e.g., drinking motives) are accounted for in multivariate models. Thus, we examined how multiple domains of acculturation were associated with alcohol use, high intensity drinking, and negative alcohol consequences, and whether direct associations and potential gender moderation of these relationships remained after drinking motives were included in the model. METHOD: = 1.85, range = 18-25) completed self-report questionnaires online. RESULTS: After accounting for demographic variables and drinking motives, U.S. cultural practices were negatively associated with alcohol use, and ethnic identity was negatively associated with high intensity drinking. Heritage cultural practices were positively associated with high intensity drinking among women only. Finally, enhancement motives were positively associated with alcohol use and high intensity drinking, while social and coping-depression motives were positively related to negative alcohol consequences, even after accounting for demographic variables and multiple acculturation domains. CONCLUSIONS: The present findings paint a nuanced picture of the effects of ethnic identity and engagement with cultural practices on drinking behaviors, particularly among Hispanic college women. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".