The association between drinking motives and alcohol‐related consequences – room for biases and measurement issues?
Bibliographic record
Abstract
AIMS: To investigate whether the predominant finding of generalized positive associations between self-rated motives for drinking alcohol and negative consequences of drinking alcohol are influenced by (i) using raw scores of motives that may weight inter-individual response behaviours too strongly, and (ii) predictor-criterion contamination by using consequence items where respondents attribute alcohol use as the cause. DESIGN: Cross-sectional study within the European School Survey Project on Alcohol and other Drugs (ESPAD). SETTING: School classes. PARTICIPANTS: Students, aged 13-16 (n = 5633). MEASUREMENTS: Raw, rank and mean-variance standardized scores of the Drinking Motives Questionnaire--Revised (DMQ-R); four consequences: serious problems with friends, sexual intercourse regretted the next day, physical fights and troubles with the police, each itemized with attribution ('because of your alcohol use') and without. FINDINGS: As found previously in the literature, raw scores for all drinking motives had positive associations with negative consequences of drinking, while transformed (rank or Z) scores showed a more specific pattern: external reinforcing motives (social, conformity) had negative and internal reinforcing motives (enhancement, coping) had non-significant or positive associations with negative consequences. Attributed consequences showed stronger associations with motives than non-attributed ones. CONCLUSION: Standard scoring of the Drinking Motives Questionnaire (Revised) fails to capture motives in a way that permits specific associations with different negative consequences to be identified, whereas use of rank or Z-scores does permit this. Use of attributed consequences overestimates the association with drinking motives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".