Longitudinal examination of alcohol demand and alcohol‐related reinforcement as predictors of heavy drinking and adverse alcohol consequences in emerging adults
Bibliographic record
Abstract
Abstract Background and Aims Behavioral economic theory predicts that high alcohol demand and high proportionate alcohol‐related reinforcement are important determinants of risky alcohol use in emerging adults, but the majority of research to date has been cross‐sectional in nature. The present study investigated prospective and dynamic relationships between alcohol demand and proportionate alcohol‐related reinforcement in relation to heavy drinking days and alcohol problems. Design Longitudinal cohort with assessments every 4 months for 20 months. Setting Ontario, Canada. Participants Emerging adults reporting regular heavy episodic drinking (n = 636, Mage = 21.44; 55.8% female). Measurements Heavy drinking days (HDD; Daily Drinking Questionnaire), alcohol problems (Brief Young Adult Alcohol Consequences Questionnaire), alcohol demand (Alcohol Purchase Task) and proportionate alcohol‐related reinforcement (Activity Level Questionnaire). Findings Linear mixed effects models revealed that behavioral economic indicators and alcohol‐related outcomes significantly decreased over the study, consistent with ‘aging out’ of risky alcohol use. Random intercept cross‐lagged panel models revealed significant between‐person relationships, such that higher alcohol demand and alcohol‐related reinforcement were positively associated with HDD and alcohol problems (random intercepts = 0.187–0.534, Ps < 0.01). Moreover, alcohol demand indicators (particularly the rate of change in elasticity of the demand curve, as measured by α, and the maximum expenditure, Omax) and proportionate alcohol‐related reinforcement significantly forecasted changes in HDD at all time points (|βs| = 0.063–0.103, Ps < 0.05) in cross‐lagged relationships, with bidirectional associations noted for the rate of change in elasticity (βs = −0.085 to −0.104, Ps < 0.01). Proportionate alcohol‐related reinforcement also significantly forecasted changes in alcohol problems at all time points (βs = 0.072–0.112, Ps < 0.01). Conclusions Multiple behavioral economic indicators (demand elasticity, maximum expenditure and reinforcement ratio) forecast changes in heavy episodic drinking and alcohol problems over the course of emerging adulthood. These results further implicate alcohol demand and proportionate alcohol‐related reinforcement as etiologically and developmentally important mechanisms in alcohol use trajectories.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".