Integrating Behavioral Economic and Social Network Influences in Understanding Alcohol Misuse in a Diverse Sample of Emerging Adults
Bibliographic record
Abstract
BACKGROUND: Behavioral economic alcohol demand is a measure of motivation to consume alcohol and a robust risk factor for alcohol misuse. Social networks that are dense with alcohol are also associated with heavy drinking, but the intersection of these risk factors has not been investigated to date. This study examined these interrelationships with structural equation modeling using cross-sectional data from a diverse community sample of heavy-drinking emerging adults (N = 602). METHODS: Latent variables for alcohol social network, alcohol demand, and alcohol misuse were constructed. Next, relations between the latent variables were examined, including the indirect effect of alcohol demand in the relation between alcohol social network and alcohol misuse. An alternative modeling testing the indirect effect of alcohol social network on the relation between alcohol demand and misuse was also tested. RESULTS: When alone in the model, social network alcohol density significantly predicted alcohol misuse. When alcohol demand was included in the model, social network alcohol density predicted alcohol demand, alcohol demand predicted alcohol misuse, and an indirect effect on alcohol misuse through alcohol demand was present. In the alternative model, an indirect effect was not present between alcohol demand and alcohol misuse by social network alcohol density. Exploratory analyses revealed significant sex, race, and college status differences. CONCLUSIONS: These results suggest that the influence of social network alcohol density on alcohol misuse may be, in part, through variance accounted for by alcohol reinforcing value. Longitudinal testing of this mechanistic pathway is warranted.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".