Factors Associated with Treatment Compliance and its Effects on Retention among Participants in a Court-Mandated Treatment Program
Bibliographic record
Abstract
Drug treatment court (DTC) programs have been implemented and promoted in American as well as Canadian judicial systems as an effective tool for reducing criminal recidivism rates. An evaluation of the program in Toronto revealed that the drug court participants' substance abuse and criminal behaviors are reduced while they are under the drug courts' jurisdiction, and to some extent recidivism is reduced after participants leave the program. However, while we know from the literature that there are positive effects of the program, the characteristics of drug-dependent offenders who benefit the most from the DTC are less clear. The main purpose of this study was to understand where the prediction from literature, that compliance determines success in treatment, fails. Thus, study participants were divided into two groups: 1) those who might normally be expected to not comply yet who do in the long run (unexpected retention, UR); and 2) those who might normally be expected to comply, but who do not (unexpected expulsions, UE). Discriminant function analysis showed that participants considered UR were subject to conditions of social disadvantage yet quite motivated; whereas UE participants had no housing concern, no indication of family problems, but had additional criminal justice involvement at an early stage of the program. Implications for strengthening DTCs as well as suggestions for future research in the drug treatment and drug court fields are discussed.
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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 | 0.005 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".