Integrating Injectable Opioid Agonist Treatment into A Drug Treatment Court Program: A Case Study
Bibliographic record
Abstract
BACKGROUND: A substantial proportion of individuals involved with the North American criminal justice system are convicted for drug-related activities. Drug treatment court (DTC) programs were developed as an alternative to incarceration for drug-related offences and aim to prioritize addiction treatment and improve health and social outcomes; however, only a fraction of DTC participants have access to first-line medications for opioid use disorder (OUD). Further, despite emerging evidence for the efficacy of injectable opioid agonist therapy (OAT) in treating individuals with severe OUD where past treatment attempts with first-line therapies have been unsuccessful, this treatment has never, to our knowledge, been implemented in correctional settings. CASE: An individual in their 50s with a history of severe OUD, multiple interactions with the criminal justice system, and prior unsuccessful treatment attempts with methadone was initiated on injectable treatment with diacetylmorphine. The patient received 300 mg of diacetylmorphine witnessed 3 times daily at a supervised injection clinic. During a 1.5-year stabilization phase, the patient's illicit opioid use significantly reduced. They subsequently enrolled in a DTC program for drug-related charges preceding initiation on injectable OAT and remained on this therapy during 16 months in DTC. Following graduation from DTC, the patient continued to receive treatment and returned to gainful employment in the community, with no further charges or episodes of incarceration. DISCUSSION: This case describes the successful completion of a DTC program by an individual prescribed injectable OAT for severe OUD. The patient's treatment plan played an integral role in DTC graduation and long-term adherence, leading to improved health and social outcomes, including cessation of illicit drug use, enhanced quality of life, and improved social functioning. The case highlights the potential benefits of a stepped and integrated approach to addiction treatment in DTC programs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| 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".