Drug use during pregnancy: Validating the Drug Abuse Screening Test against physiological measures.
Bibliographic record
Abstract
This study examined the ability of the Drug Abuse Screening Test (DAST-10) to identify prenatal drug use using hair and urine samples as criterion variables. In addition, this study was the first to use "best practices," such as anonymity, ACASI technology, and a written screener, to facilitate disclosure in this vulnerable population. 300 low-income, post-partum women (90.3% African-American) were recruited from their hospital rooms after giving birth. Participation involved (a) completing a computerized assessment battery that contained the DAST-10 and (b) providing urine and hair samples. Twenty-four percent of the sample had a positive drug screen. The sensitivity of the DAST-10 was only .47. Nineteen percent of the sample had a positive toxicology screen but denied drug use on the DAST-10. Findings suggest that brief drug use screeners may have limited utility for pregnant women and that efforts to facilitate disclosure via reassurance and anonymity are unlikely to be sufficient in this population.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".