Psychometric Synthesis of the Drug Abuse Screening Test (DAST) Versions
Bibliographic record
Abstract
Problem Statement Among individuals aged 12 years or older, 14.3% (40.0 million) reporting the use of an illicit drug in the previous year. Given the prevalence of drug abuse, it is increasingly important to determine effective screening practices, treatment procedures, and best practices among various subpopulations to identify drug use-related consequences. The DAST is one of the most commonly used and accurate drug screening tests.Method This psychometric synthesis of four versions of the Drug Abuse Screening Test (DAST-10, DAST-20, DAST-28, DAST-A) provided aggregated evidence from 346 articles over 40 years of published literature for score reliability, structure, diagnostic, and convergent validity, and descriptive statistics, all with the goal of informing counseling and medical practice and research.Results Results indicated adequate internal consistency (α = 0.81–0.84 across all four versions) and mostly medium to large effect size convergent correlations with comparison measures. Aggregated diagnostic validity data indicate optimal cutoff scores of 7 for DAST-10, 8 for DAST-20, 10 for DAST-28, and 6 for DAST-A.Discussion The DAST-10 appears the best choice for practical and psychometric reasons. Additional studies of the various DAST versions are needed to expand use across participant demographics.Public Statement of Relevance Drug use continues to be a societal problem and mental health practitioners need effective screening practices, treatment procedures, and best practices among various subpopulations to identify drug use-related consequences. This study synthesized 40 years of research on the four versions of the Drug Abuse Screening Test (DAST-10, DAST-20, DAST-28, DAST-A) and found acceptable levels of score reliability and validity for screening purposes.
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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 | 0.045 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".