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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".