Engaging people who use drugs in policy and program development: A review of the literature
Bibliographic record
Abstract
Health policies and programs are increasingly being driven by people from the community to more effectively address their needs. While a large body of evidence supports peer engagement in the context of policy and program development for various populations, little is known about this form of engagement among people who use drugs (PWUD). Therefore, a narrative literature review was undertaken to provide an overview of this topic. Searches of PubMed and Academic Search Premier databases covering 1995-2010 were conducted to identify articles assessing peer engagement in policy and program development. In total, 19 articles were included for review. Our findings indicate that PWUD face many challenges that restrict their ability to engage with public health professionals and policy makers, including the high levels of stigma and discrimination that persist among this population. Although the literature shows that many international organizations are recommending the involvement of PWUD in policy and program development, our findings revealed a lack of published data on the implementation of these efforts. Gaps in the current evidence highlight the need for additional research to explore and document the engagement of PWUD in the areas of policy and program development. Further, efforts to minimize stigmatizing barriers associated with illicit drug use are urgently needed to improve the engagement of PWUD in decision making processes.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".