Rasch Analysis of Cannabis Use Disorder in an Adult Inpatient Sample
Bibliographic record
Abstract
Objective: The Diagnostic and Statistical Manual of Mental Disorders version 5 (DSM-5) defines cannabis use disorder as a polythetic unidimensional diagnosis (>2 symptoms from up to 11), but few studies have empirically evaluated the latent structure of CUD. Rasch analysis is a psychometric technique that has previously been used to validate unidimensional scales, like DSM-5 CUD. Method: In this study, the Rasch model was used to evaluate the DSM-5 CUD criteria in a clinical sample of adults receiving inpatient treatment for substance use disorder (n = 249) reporting active cannabis use at admission. The unidimensionality of the criteria was evaluated using the Martin-Löf test and the nonparametric –T2 test of Ponocny. Model fit was assessed using the χ2 goodness of fit test for individual items. Results: Results supported the unidimensional structure of the criteria. Symptom # 3 was the least endorsed, highest severity item. Conversely, symptom #9 was the most endorsed and had the lowest severity estimate. Overall, the data fit the Rasch model well, although misfit was observed for symptom # 8. Conclusions: Rasch's analysis of CUD symptoms in an inpatient sample broadly supports the DSM-5 CUD syndrome. Further examination is needed to determine if removing or revising the hazardous use symptom criterion in future DSM revisions would improve diagnostic measurement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".