The DRINC (Drinking Reasons Inter-National Collaboration) project: Rationale and protocol for a cross-national study of drinking motives in undergraduates
Bibliographic record
Abstract
Drinking motives are a proximal predictor of alcohol use and misuse through which the effects of more distal influences (e.g., personality) on alcohol-related outcomes are mediated. Although Cooper’s (1994) four-factor drinking-motives model has been well validated in North America, few studies have validated this model in other countries. The aim of the present paper is to describe the rationale, protocol, and methods of a project designed to evaluate the cross-national validity and generalizability of Cooper’s (1994) measure, as modified by Kuntsche and Kuntsche’s Drinking Motives Questionnaire Revised Short Form (DMQ–R SF, 2009), and of the theoretical model (Cooper, Frone, Russell, & Mudar, 1995) linking drinking motives to specific personality risks and alcohol consequences. The project uses data from undergraduates representing 10 nations (Brazil, United Kingdom and Republic of Ireland, Canada, Hungary, Mexico, the Netherlands, Portugal, Spain, Switzerland, and the United States; total N = 8,478). Findings from this collaboration can be used to guide international researchers in determining the suitability of the DMQ–R SF as a measure of drinking motives in countries outside of North America and may have implications for the development of preventive and therapeutic interventions for alcohol misuse among young adults globally.
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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 | 0.073 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.019 |
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".