The Impact of Injectable Opioid Agonist Treatment (iOAT) on Involvement in Criminalized Activities: A Secondary Analysis from a Clinical Trial in Vancouver, BC
Bibliographic record
Abstract
Purpose: A significant portion of the economic consequences of untreated Opioid Use Disorder (OUD) relate to individuals' involvement in the criminal justice system. The present study uncovers if treatment with iOAT is related to the number of criminal charges amongst participants, what type of crime participants were involved in, and the frequency with which participants were victims of crime. This study contributes to the body of research on the effectiveness of iOAT reducing criminal involvement. Patients and Methods: This is a secondary analysis of police record data obtained from the Vancouver Police Department over a three-year period during the Study to Assess Longer-term Opioid Medication Effectiveness clinical trial. The data was obtained from participants (N = 192) enrolled in the trial through a release of information form. Results: During the three-year period, most charges (45.6%) were property offences, and 25.5% of participants were victims of crime. Participants with no treatment prior to randomization into the SALOME trial were 2.61 (95% CI = 1.64-4.14) more likely to have been charged with a crime than during the iOAT state. Conclusion: IOAT can reduce individuals' involvement with the criminal justice system and is thus a crucial part of the continuum of care. Addiction should be conceptualized as a healthcare rather than criminal issue.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".