Implementation facilitators and barriers to the expansion of a peer-led overdose prevention program
Bibliographic record
Abstract
In Canada, there has been a substantial increase of opioid overdoses in recent years. PROFAN, a peer-led overdose prevention initiative, was successfully implemented in Montreal, Quebec, for people who use drugs (PWUD), or those likely to witness overdoses. The worsening of the situation during the COVID-19 pandemic sparked the need to expand the program across the province. Individual interviews were conducted with 17 key informants from 12 health regions to identify implementation facilitators and barriers. A thematic analysis was conducted based on emerging themes. Four main facilitators were reported: 1) presence of an active peer network involved with harm reduction in the region, 2) collaboration among community and public health sectors, 3) stakeholders’ awareness of opioid situation, and 4) perceived appropriateness of training. Six main barriers also emerged: 1) geographical isolation, 2) existing offer of similar services, 3) difficulty reaching isolated PWUD, 4) stigmatization of PWUD, 5) unwillingness of stakeholders to address situation, and 6) lack of funding stability. The expansion achieved by the PROFAN program highlights the ability of a peer-driven community organization to lead an overdose prevention program when provided with funding and support from government health agencies and partnerships with other organizations such as addiction worker associations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".