Drug Treatment Courts: A Quantitative Review of Study and Treatment Quality
Bibliographic record
Abstract
The effectiveness of drug courts has been the subject of numerous studies, and three major meta-analyses have examined many of these studies in regard to two main factors: (1) study quality and (2) treatment quality. The current study examines these two factors more closely. Study quality was assessed using the Collaborative Outcome Data Committee Guidelines (CODC); studies were rated as “rejected,” “weak,” “good,” or “strong” based on methodological quality. Drug court treatment quality was assessed by evaluating adherence to the principles of Risk-Need-Responsivity (RNR). The RNR principles have been previously shown to mediate the effectiveness of offender treatment across various offender groups and a variety of criminogenic needs. In total, 96 studies were reviewed and assessed according to study and treatment quality. Results found that the study quality of the literature is poor and that this accounts for much of the variability in findings seen across studies. Furthermore, analyses revealed that although adherence to the RNR principles was poor, increasing adherence to RNR resulted in more effective treatment of offenders and reduced recidivism. Using only methodologically acceptable studies, the least biased estimate of the effectiveness of drug courts in reducing recidivism was found to be approximately 8%.
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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 | 0.105 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".