Differences in Alcohol-Related Variables Between Individuals Who Engage in Food and Alcohol Disturbance (FAD) Behaviors and Those Who Only Use Alcohol: The Role of FAD-Motives
Bibliographic record
Abstract
OBJECTIVE: Food and alcohol disturbance (FAD) is the use of any compensatory behavior (e.g., skipping meals) within the context of a drinking episode. FAD has two underlying motives: to enhance the effects of alcohol (FAD-AE) and/or compensate for calories consumed from alcohol (FAD-CC). Prior work finds that FAD is positively associated with alcohol-related outcomes; however, it is unclear whether FAD confers increased risk above alcohol use alone and whether there are differences in alcohol outcomes by FAD-motive. Thus, the present study evaluated alcohol use patterns (i.e., past-month quantity/frequency, binge use, consequences, and drinking motives) by FAD status and FAD-motives. METHOD: Data were from the Stimulant Norms and Prevalence 2 (SNAP2) study, which included 5,809 undergraduates from six U.S. universities. Participants were grouped into four categories: Alcohol-Only, FAD-AE, FAD-CC, and FAD-both (i.e., both FAD-AE and FAD-CC motives). Ordinary least squares regression was used for drinking motives, and quasi-Poisson regressions were used for other outcomes. RESULTS: Alcohol use quantity, frequency, binge use, and consequences were all greatest in the FAD-both group and lowest in the alcohol-only group, with the FAD-AE and FAD-CC groups intermediate and not significantly different from each other. To illustrate, the FAD-both group had 47%, 33%, and 25% greater alcohol-related consequences than the alcohol-only, FAD-CC, and FAD-AE groups, respectively. This stepwise pattern held for drinking motives, with fewer significant differences. CONCLUSIONS: Engagement in FAD is linked to an increased likelihood of poor alcohol outcomes versus alcohol use alone, and FAD for both motives represents the highest risk group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".