Drinking Motives in Clinical and General Populations
Bibliographic record
Abstract
AIMS: This paper had three aims: (1) to validate a Spanish adaptation of the Modified Drinking Motives Questionnaire-Revised (M DMQ-R), (2) to explore the relationship of each drinking motive with different patterns of alcohol use, and (3) to compare the drinking motives of moderate drinkers, heavy drinkers, and alcohol abusing/dependent individuals. METHODS: Two studies were carried out. In Study 1, a sample of 488 participants completed the M DMQ-R and a self-report scale of alcohol consumption in order to study the factor structure and different indices of reliability and validity of the Spanish M DMQ-R. In Study 2, we compared the drinking motives of moderate and heavy drinkers from Study 1 and an additional sample of 59 clinical drinkers. RESULTS: The M DMQ-R demonstrated sound reliability and validity indices. Coping-with-anxiety, social, and enhancement motives predicted higher alcohol use on weekends, but only coping-with-anxiety and social motives were related to consumption on weekdays. Furthermore, moderate drinkers had the lowest scores for all motives, whereas alcohol-dependent participants obtained the highest scores for negative reinforcement drinking motives. CONCLUSION: The Spanish M DMQ-R is a reliable and valid measure of drinking motives and has potential for assisting with treatment planning for problem drinkers.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".