Quantifying cannabis problems among college students from English and Spanish speaking countries: Cross-cultural validation of the Cannabis Use Disorders Identification Test-Revised (CUDIT-R)
Bibliographic record
Abstract
OBJECTIVE: The Cannabis Use Disorders Identification Test - Revised (CUDIT-R) is a broadly employed measure of cannabis-related problems. However, minimal research has tested the measurement invariance of the CUDIT-R among youths from different countries, hindering cross-national comparisons. Thus, the present study aimed to test the measurement invariance of the CUDIT-R between seven countries and gender groups, and provide different sources of reliability and validity evidence of the scale. METHODS: A sample of 4,712 college student lifetime cannabis users (mean age = 20.57, SD = 3.97; 70.4% females) from seven countries completed the CUDIT-R. Last 30-day cannabis users (n = 2402; mean age = 20.09, SD = 3.18; 67.7% females) additionally completed another measure of cannabis-related problems, and measures of cannabis frequency, quantity and motives. RESULTS: Multigroup analysis showed configural (equal number of factors and pattern of factor-indicator relationships), metric (equal factor loadings) and scalar (equal thresholds) invariance of the CUDIT-R across five countries and across gender in the sample of lifetime cannabis users. Cronbach's alphas and ordinal omegas ranked from .72 and .85. Large correlations were found between the CUDIT-R and another cannabis-related problem scale. Small to large associations were found between the CUDIT-R and other criterion variables (frequency and quantity of consumption and cannabis-related motives) providing convergent and discriminant validity evidence. Only a few differences in the magnitude of the correlations across countries were found. CONCLUSIONS: The results suggest that the CUDIT-R is a suitable measure to assess cannabis-related problems among college student from the U.S., Canada, South Africa, Spain, and Argentina and across gender groups.
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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 | 0.005 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".