Barriers to retention in the Toronto Drug Treatment Court program: what provides the impetus to succeed or to fail?
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 recidivism rates. 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 that there are positive effects of the program, the characteristics of drug-dependent offenders who benefit the most or the least from the DTC are less clear. A path analysis model showed that factors responsible for success or failure in program participation depend on past criminal history. Lack of stable housing was significantly associated with clients' retention. As expected, the stages of change data effectively predicted dropout status. Cox regression found predictors of dropouts such as, young age, criminal record, unemployment, and new re-offenses during program. A logistic regression model showed that clients considered 'unexpected retainers' were subject to conditions of social disadvantage yet quite motivated; whereas clients considered 'unexpected dropouts' had no housing concern, no indication of family problems but had criminal justice involvement in early stage of the program. Implications for the Toronto DTC, as well as suggestions for future research in the drug treatment and drug court fields are discussed. The purpose of this study was to identify those factors that determine study participants' expulsion or involuntary dismissal and long-term retention in the Toronto DTC. This was approached in three ways. The first approach was to investigate statistically significant factors that might predict retention and re-offence using latent path analysis. The second approach tested whether clients in higher stages of change remain in the program longer than those in lower stages of change and to predict what factors were responsible for dropping out of the program using survival analysis. The final approach was to identify characteristics and factors among clients who might normally be expected to not comply yet who do in the long run [unexpected retainers]; and who might normally be expected to comply, but who do not [unexpected dropouts].
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".