Substance abuse treatment and pressures from the criminal justice system: data from a provincial client monitoring system
Bibliographic record
Abstract
AIMS: Compulsory treatment is discussed increasingly as a way to reduce the population burden of addictive behaviours. This study explores the extent to which social control strategies exercised through the criminal justice system are used to bring people into substance abuse treatment at a system level. We also assessed whether particular subgroups may be more or less likely to be brought into treatment in this manner. DESIGN: We employed a secondary analysis of data from a client-based information system which captured demographic, referral and substance use characteristics from people seeking treatment for substance abuse. PARTICIPANTS: A census of clients (n = 45123) entering specialized Ontario addiction treatment programmes between 1 April 1999 and 31 March 2000. FINDINGS: Some 28.9% of clients reported legal problems at treatment intake, and 13.9% had an explicit corrections-related condition of treatment contact. Logistic regression analyses indicated that legal problems and corrections-related conditions of treatment were more prevalent among younger, unmarried and unemployed males, who had not completed high school. A number of important interactions were identified between these factors and substance of abuse. CONCLUSIONS: Implications for equity, accessibility and effectiveness of substance abuse treatment are discussed in relation to the tendency of treatment mandates from criminal justice system to disproportionately affect the entry of this segment of substance-abusing clients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".