Barriers to Needle Exchange Success in Canadian Prisons
Bibliographic record
Abstract
In 2018, Correctional Service Canada rolled out the Prison Needle Exchange Program (PNEP) in response to growing human rights and public health concerns surrounding the spread of HIV and HCV in Canadian prisons. Despite having goals to save lives and reduce harm, an independent program evaluation in 2020 revealed that numerous prisons had zero program participants and, as of 2022, fewer than fifty individuals were participating in the program. In 2019, members of the University of Alberta Prison Project (UAPP: PI Drs. Bucerius and Haggerty) interviewed 15 prison staff and 63 incarcerated women and surveyed 74 prison staff at one of Canada’s federal women’s prisons regarding their views of substance use and harm reduction strategies. Using a mixed-methods research approach, I conduct a thematic analysis of these qualitative interviews to identify perceptions toward the prison needle exchange program. Further, I augment qualitative insights with findings from the institution-wide survey of 74 prison staff, including 38 correctional officers and 36 other staff. In Paper 1, I use an implementation science research perspective, which aims to translate research findings into practice, identifying numerous barriers to the PNEPs’ successful implementation. My findings reveal an overwhelming lack of support toward the PNEP for both prison staff and incarcerated women. Interviews reveal three main themes for such disapproval: organizational barriers to program uptake, subcultural barriers to program uptake, and health and safety concerns. In Paper 2, I run a stepwise linear regression analysis of 74 prison staff to understand what factors influence staff approval toward the PNEP, with several notable results. My findings show significant differences between correctional officers and other staff, with correctional officers being more disapproving toward the PNEP. Interestingly, prison staff’s familiarity with harm reduction programs has a negative relationship with PNEP approval, with further analysis revealing this relationship is only significant for correctional officers. This finding suggests a different understanding of “familiarity” between correctional officers and other staff, which may be informed by their unique lived experiences and work responsibilities. Perceptions of harm reduction program effectiveness has a significant positive relationship with PNEP approval, which suggests the more effective staff perceive programs, the higher approval they will have toward such programs. Overall, my research reveals significant barriers to the PNEPs’ success, identifying a lack of support from both incarcerated women and correctional staff. These findings are consistent with previous literature, which highlight numerous problems with PNEP design and implementation. I conclude with recommendations for future research and possible reforms to improve implementation fidelity moving forward.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| 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".