Using nominal group technique to identify perceived barriers and facilitators to improving uptake of the Prison Needle Exchange Program in Canadian federal prisons by correctional officers and healthcare workers
Bibliographic record
Abstract
Background: Elimination of bloodborne viruses including HIV and hepatitis C virus from prisons requires high coverage of evidence-based interventions that prevent bloodborne virus transmission, including needle and syringe programs. Canada launched a Prison Needle Exchange Program (PNEP) in nine federal prisons in 2018; however, uptake among people who inject drugs in prison remains low. We aimed to explore barriers and facilitators to improving PNEP uptake identified by correctional officers and healthcare workers. Methods: Participants from nine federal prisons with PNEP completed focus groups using nominal group technique, a rapid mixed-method consensus strategy. Responses were generated, rank-ordered, and prioritized by each stakeholder group. We identified the highest-ranking responses (≥10 % of the overall votes) to questions about barriers and facilitators to PNEP uptake. Results: Between September 2023 and February 2024, 16 focus groups were conducted with 118 participants (n = 51 correctional officers; n = 67 healthcare workers). Among correctional officers, the top perceived barriers were bullying from peers (22 %), fear of being targeted by correctional officers (14 %), and fear of repercussions due to drug use (13 %). The top facilitators were safe injection sites (30 %), provision of wrap-around services (16 %), and education of correctional officers (10 %). Among healthcare workers, the top perceived barriers were lack of confidentiality (16 %), fear of being targeted by correctional officers (12 %), and a long and complex application process (11 %). The top facilitators were education of correctional officers (29 %), delivery of PNEP by an external provider (15 %), automatic approval for participation in the PNEP (13 %), and safe injection sites (12 %). Conclusion: Multiple modifiable barriers and solutions to improving PNEP uptake in Canadian federal prisons were identified by correctional employees. Both participant groups identified the potential for safe injection sites and education to correctional officers as enabling PNEP uptake. These data will inform Canadian efforts to improve engagement and to expand PNEP coverage.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".