Barriers and facilitators to opioid agonist treatment (OAT) engagement among individuals released from federal incarceration into the community in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Correctional populations with opioid use disorder experience increased health risks during community transition periods. Opioid Agonist Treatment (OAT) can reduce these risks, but retention is a key challenge. This study addresses a knowledge gap by describing facilitators and barriers to OAT engagement among federal correctional populations released into the community in Ontario, Canada. METHODS: This article describes results from a longitudinal mixed-methods study examining OAT transition experiences among thirty-five individuals released from federal incarceration in Ontario, Canada. Assessments were completed within one year of participants' release. Data were thematically analyzed. RESULTS: The majority (77%) of participants remained engaged in OAT, however, 69% had their release suspended and 49% returned to custody. Key facilitators for OAT engagement included flexibility, positive staff rapport, and structure. Fragmented OAT transitions, financial OAT coverage, balancing reintegration requirements, logistical challenges, and inaccessibility of 'take-home' OAT medications were common barriers. CONCLUSIONS: Post-incarceration transition periods are critical for OAT retention, yet individuals in Ontario experience barriers to OAT engagement that contribute to treatment disruptions and related risks such as relapse and/or re-incarceration. Additional measures to support community OAT transitions are required, including improved discharge planning, amendments to OAT and financial coverage policies, and an expansion of OAT options.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".