Do DSM-5 substance use disorder criteria differ by user care settings? An item response theory analysis approach
Bibliographic record
Abstract
AIM: To examine differences in the psychometric characteristics of diagnostic criteria for Substance Use Disorders (SUD) between substance users in harm reduction settings (HR) and substance users seeking treatment (Tx). METHODS: Differential Item and Test Functioning (DIF & DTF) analysis were performed to examine differences in the difficulty of endorsement and in discrimination of the 11 diagnostic criteria and to test if the criteria set as a whole (the "test") functioned differently by care settings (Tx vs. HR) for alcohol, cocaine, cannabis, opiates and tobacco. To test uniform and nonuniform DIF, multiple indicator multiple cause (MIMIC) structural equation models were used. RESULTS: Regardless of the substance, the DSM-5 criteria "craving", "large amount", "time spent", "tolerance" and "activities given up" had similar functioning by care settings. Little evidence for DIF was found for other criteria. The criteria set as a whole did not function differently by care settings for alcohol, cocaine and tobacco. At the same trait severity, compared to HR, the Tx subgroup had a greater number of endorsed criteria for cannabis and a smaller number of endorsed criteria for opioids. CONCLUSION: The unidimensionality of the 11 DSM-5 criteria and applicability of all criteria and diagnosis was confirmed in this large sample of problematic substance users. While the majority of the criteria related to loss of control of substance use, functioned well in both care settings, the criteria related to consequences of substance use had several differential functioning.
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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 | 0.033 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".