“How are we gonna get them off the drugs if they’re allowed to stay on it?” correctional officer perspectives on Overdose prevention sites in prisons
Bibliographic record
Abstract
Despite growing support for Overdose Prevention Sites (OPS) in global communities, there is less international support for their implementation in prisons. To interpret the contexts shaping positionalities on and challenges associated with OPS in prisons, in the current study, we analyze interpretations of federal correctional officers (COs) in Canada (n = 134) on OPS in prison and associated harm reduction measures. Data were collected through a longitudinal, semi-structured interview research design. Results indicate how many participants support OPS, especially when caveated as a preference over the Prison Needle Exchange Program (PNEP). Still, participants described challenges and complications with OPS policy, implementation, and safety concerns; namely, that OPS hinder correctional rehabilitation, recovery from substance misuse, and effective reintegration post release. While some COs express understanding and support for harm reduction initiatives such as OPS, they called for clear directives and policies, which will support hesitant staff in facilitating this public health measure in prison settings. We untangle policy requirements and raise a number of key questions to support the successful implementation of prison OPS from the perspective of officers, specifically around issues related to needle possession, liability of officers, substance confiscation and the prison economy, and the health and rehabilitation of incarcerated people.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".