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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".