Using the socioecological model to guide service delivery improvements to the prison needle exchange program in Canada: insights from multi-level stakeholders
Bibliographic record
Abstract
BACKGROUND: In 2018-2019, Canada introduced a Prison Needle Exchange Program (PNEP) across nine federal facilities to mitigate the harms associated with drug injection among incarcerated people. However, program uptake has been limited. We explored the barriers and facilitators to improving PNEP services among key stakeholders in prison. METHODS: Stakeholders in nine federal prisons with active PNEP participated in focus groups using nominal group technique to achieve rapid consensus. Responses were generated, rank-ordered, and prioritized by each stakeholder group (correctional officers, healthcare workers, and people in prison). We identified the highest-ranking responses to questions about barriers and solutions to PNEP uptake and described them using the five levels of the Socioecological Model: individual, interpersonal, organizational, system, and structural/policy. RESULTS: Between September 2023 and February 2024, 34 focus groups were conducted with 215 participants (n = 51 correctional officers (24%); n = 67 healthcare workers (31%); n = 97 people in prison (45%)). Key barriers identified were lack of confidentiality and privacy across all levels and fear of repercussions from drug use and fear of being targeted at the individual-interpersonal levels. Preferred solutions included comprehensive education across all levels, and establishment of supervised/safe injection sites and external program management, potentially involving peers, at the structural level. CONCLUSIONS: Several multi-level modifiable barriers to improving PNEP uptake in Canadian federal prisons were shared among key stakeholders. Structural changes to PNEP delivery, including supervised/safe injecting sites and peer-led programs, were proposed as solution-driven enablers to increasing PNEP uptake among incarcerated people who inject drugs. These data will inform Canadian efforts to expand PNEP provision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".