Associations between the Brief Assessment of Alcohol Demand (BAAD) questionnaire and alcohol use disorder severity in UK samples of student and community drinkers
Bibliographic record
Abstract
Value based choice and compulsion theories of addiction offer distinct explanations for the persistence of alcohol use despite harms. Choice theory argues that problematic drinkers ascribe such high value to alcohol that costs are outweighed, whereas compulsion theory argues that problematic drinkers discount costs in decision making. The current study evaluated these predictions by testing whether alcohol use disorder (AUD) symptom severity (indexed by the AUDIT) was more strongly associated with the intensity item (maximum alcohol consumption if free, indexing alcohol value) compared to the breakpoint item (maximum expenditure on a single drink, indexing sensitivity to monetary costs) of the Brief Assessment of Alcohol Demand (BAAD) questionnaire, in student (n = 579) and community (n = 120) drinkers. The community sample showed greater AUD than the student sample (p = .004). In both samples, AUD severity correlated with intensity (students, r = 0.63; community, r = 0.47), but not with breakpoint (students, r = −0.01; community, r = 0.12). Similarly, multiple regression analyses indicated that AUD severity was independently associated with intensity (student, ΔR2 = 0.20, p < .001; community, ΔR2 = 0.09, p = .001) but not breakpoint (student, ΔR2 = 0.003, p = .118; community ΔR2 = 0.01, p = .294). There was no difference between samples in the strength of these associations. The value ascribed to alcohol may play a more important role in AUD severity than discounting of alcohol-associated costs (compulsivity), and there is no apparent difference between student and community drinkers in the contribution of these two mechanisms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".