Dual Substance Abusers Seeking Treatment: Demographic, Substance-Related, and Treatment Utilization Characteristics
Bibliographic record
Abstract
High comorbidity exists between alcohol and drug-related disorders. However, little information is available on characteristics of clients abusing both alcohol and illicit drugs (so-called dual substance abusers). The proportion of dual substance abusers and their characteristics are examined in a sample of 1,626 clients seeking treatment in one of the 16 participating centers in the province of Antwerp (Belgium). More than a quarter of all clients were identified as dual substance abusers. Their characteristics correspond better to those of drug abusers than to those of alcohol abusers, but compared to the former, they are younger, more often male, use more types of illicit substances and more often use stimulating substances. Alcohol is often underestimated in substance use patterns. Thorough alcohol assessment, early intervention, and preventive actions are needed within the drug treatment system, and closer collaboration with the alcohol treatment system is absolutely essential.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".