Peer Support Specialists: An Underutilized Resource in the Criminal Justice System for Opioid Use Disorder Management?
Bibliographic record
Abstract
In the wake of North America's worsening overdose crisis, the overrepresentation of individuals incarcerated with an opioid use disorder (OUD) constitutes a population at an incredibly high risk for adverse health outcomes, including death. In response, a number of important initiatives such as the provision of opioid agonist therapy to individuals with opioid addiction while incarcerated have been implemented. Although improving access to evidence-based treatment for OUD is an obvious urgent need, equally important is the need to implement novel interventions to help reduce morbidity and mortality among this high-risk group. Peer support specialists (ie, individuals with lived or shared experience) have previously been demonstrated to effectively help clients navigate the healthcare system, reintegrate within their community, and successfully adhere to their individual treatment and recovery goals. Given the known association between individuals with an OUD and exposure to the criminal justice system, routine inclusion of peer support specialists as part of the addiction interdisciplinary care team in these settings may be an effective opportunity to improve health outcomes and prevent death among incarcerated individuals with an OUD.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".