Substance Use Behavior Among Early-Adolescent Asian American Girls: The Impact of Psychological and Family Factors
Bibliographic record
Abstract
Confronting developmental tasks and challenges associated with bridging two different cultures, Asian American adolescent girls face increasing risks for substance use. Identifying risk and protective factors in this population is essential, particularly when those factors can inform preventive programs. Guided by family interaction theory, the present cross-sectional study explored the associations of psychological and familial factors with use of alcohol, prescription drugs, and other drugs among early-adolescent Asian American girls. Between August 2007 and March 2008, 135 pairs of Asian American girls (mean age 13.21 years, SD=0.90) and their mothers (mean age 39.86 years, SD=6.99) were recruited from 19 states that had significant Asian populations. Girls and mothers each completed an online survey. Relative to girls who did not use substances, girls who did had higher levels of depressive symptoms, perceived peer substance use, and maternal substance use. Multiple logistic regression modeling revealed that they also had significantly lower levels of body satisfaction, problem-solving ability, parental monitoring, mother-daughter communication, family involvement, and family rules about substance use. Household composition, acculturation, and academic achievement were not associated with girls' substance use. These findings point to directions for substance abuse prevention programming among Asian American girls.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".