Psychometric properties of the modified Drug Abuse Screening Test Sinhala version (DAST-SL): evaluation of reliability and validity in Sri Lanka
Bibliographic record
Abstract
BACKGROUND: Psychoactive drug use is an important public health issue in Sri Lanka as it causes substantial health, social and economic burden to the country. Screening for substance use disorders in people who use drugs is vital in preventive health care, as it can help to identify problematic use early. Screening can aid in referring those in need, for the most appropriate treatment and care. Thus, preventing them from developing severe substance use disorders with complications. The Drug Abuse Screening Test (DAST-10) is an evidence-based tool widely used to assess the severity of psychoactive drug use. This study aimed to culturally adapt and evaluate the validity and reliability of the Drug Abuse Screening Test (DAST-10) in Sri Lanka. METHODS: The DAST-10 was culturally adapted, and the nine-item Sinhala version (DAST-SL) was validated using exploratory and confirmatory factor analysis. The validation study was conducted in the Kandy district among people who use drugs, recruited using respondent-driven sampling. Criterion validity of the questionnaire was assessed by taking the diagnosis by a psychiatrist as the gold standard. Cut-off values for the modified questionnaire were developed by constructing Receiver Operating Characteristic (ROC) curves. The reliability of the DAST-SL was assessed by measuring its internal consistency and test re-test reliability. RESULTS: The validated DAST-SL demonstrated a one-factor model. A cut-off value of ≥ 2 demonstrated the presence of substance use disorder and had a sensitivity of 98.7%, specificity of 91.7%, a positive predictive value of 98.8% and a negative predictive value of 91.3%. The area under the curve of the ROC curve was 0.98. A cut-off score of ≤ 1 was considered a low level of problems associated with drug use. The DAST-SL score of 2-3 demonstrated a moderate level of problem severity, a score of 4-6 demonstrated a substantial level of problems, and a score of ≥ 7 demonstrated a severe level of drug-related problems. The questionnaire demonstrated high reliability with an internal consistency of 0.80 determined by Kuder-Richardson Formula-20 and an inter-class correlation coefficient of 0.97 for test re-test reliability. CONCLUSION: The DAST-SL questionnaire is a valid and reliable tool to screen for drug use problem severity in people who use drugs in Sri Lanka.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".