Understanding racial/ethnic differences in e-cigarette outcome expectancies among early adolescents: findings from the Adolescent Brain Cognitive Development Study
Bibliographic record
Abstract
Introduction E-cigarette expectancies, which may differ by race/ethnicity, play a crucial role in shaping youth e-cigarette use. Observed differences by race/ethnicity, however, may reflect racial/ethnic variations in social determinants of health, such as socioeconomic status (SES). This study examined the extent to which race/ethnicity was uniquely associated with youths’ positive and negative e-cigarette expectancies, after adjusting for SES and neighborhood disadvantage, and individual, family, and peer risk factors. Methods Analyses included 8,814 Black (15.0%), Latinx (22.8%), and White (62.2%) 12 to 14-year-old participants in the Adolescent Brain Cognitive Development Study. Applying a three-stage analytic approach, hierarchical regression analyses examined associations of positive and negative e-cigarette expectancies with race/ethnicity in three blocks, with age and gender in block 1, adding SES and neighborhood disadvantage in block 2, and individual, family, and peer risk factors in block 3. Results Black and Latinx (relative to White) race/ethnicity and Latinx (relative to Black) race/ethnicity were associated with positive expectancies (p < 0.001) in blocks 1 and 2 but were non-significant in block 3. Black and Latinx (relative to White) race/ethnicity and Latinx (relative to Black) race/ethnicity were associated with lower negative expectancies (p < 0.001) in block 1, but were no longer significant after adding SES and neighborhood indicators in block 2. Perceived risk, perceived peer disapproval, and curiosity about e-cigarettes were associated with positive and negative expectancies. Discussion The results highlight the importance of considering associations of race/ethnicity with e-cigarette expectancies in the context of social determinants and individual and interpersonal factors in e-cigarette 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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".