How Stable Is the Motive–Alcohol Use Link? A Cross-National Validation of the Drinking Motives Questionnaire Revised Among Adolescents From Switzerland, Canada, and the United States
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate cross-national differences (1) in the four-dimensional factor structure of drinking motives; (2) in the mean levels of enhancement, coping, social, and conformity motives; and (3) in the association of these motives with adolescent alcohol use, risky single-occasion drinking, and alcohol-related problems. METHOD: Confirmatory factor analysis, analysis of variance, and structural equation modeling were applied to sample data from Switzerland (n=5,118; mean age=15.3), Canada (n=2,557; mean age=15.7), and the United States (n=607; mean age=15.7). RESULTS: The results showed that the four-dimensional factor structure of the Drinking Motives Questionnaire Revised (DMQ-R) was structurally invariant across the three countries. Although the rank order in mean levels of motive endorsement was the same across countries (i.e., highest for social, followed by enhancement, coping, and conformity), the absolute levels of endorsement were highest in the Canadian sample, followed by the Swiss and then the U.S. sample. In all three countries, enhancement and coping motives were positively related to alcohol use and to risky drinking in particular, and coping motives were additionally related to alcohol-related problems. CONCLUSIONS: The results indicate that the DMQ-R is a valid and reliable instrument to assess drinking motives across cultures. It appears therefore that the DMQ-R is an ideal instrument for inclusion in large cross-national surveys and that programs that target motives as a way to reduce risky drinking may be appropriate for different drinking cultures in different geographical locations.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".