Social epidemiology of early adolescent alcohol expectancies
Bibliographic record
Abstract
PURPOSE: To determine the sociodemographic correlates of alcohol expectancies (i.e., beliefs regarding positive or negative effects of alcohol) in a national (U.S.) cohort of early adolescents 10-14 years old. A second aim was to determine associations between alcohol sipping and alcohol expectancies. METHODS: We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,868; Year 2). Linear regression analyses were conducted to estimate associations between sociodemographic factors (sex, race/ethnicity, sexual orientation, household income, parental education, parent marital status, religiosity) and positive (e.g., stress reduction) and negative (e.g., loss of motor coordination) alcohol expectancies. Additional linear regression analyses determined associations between alcohol sipping and alcohol expectancies, adjusting for sociodemographic factors. RESULTS: Overall, 48.8% of the participants were female and 47.6% racial/ethnic minorities, with a mean age of 12.02 (SD 0.67) years. Older age among the early adolescent sample, male sex, and sexual minority identification were associated with more positive and negative alcohol expectancies. Black and Latino/Hispanic adolescents reported less positive and negative alcohol expectancies compared to White non-Latino/Hispanic adolescents. Having parents with a college education or greater and a household income of $200,000 and greater were associated with higher positive and negative alcohol expectancies. Alcohol sipping was associated with higher positive alcohol expectancies. CONCLUSIONS: Older age, White non-Latino/Hispanic race, male sex, sexual minority status, higher parental education, and higher household income were associated with higher positive and negative alcohol expectancies. Future research should examine the mechanisms linking these specific sociodemographic factors to alcohol expectancies to inform future prevention and intervention efforts.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".