Capacity Building and Creating Supportive Environments for Inclusive Community-Based Participatory Research: A Case Study of the Overdose Prevention Peer Research Assistant (OPPRA) Project
Bibliographic record
Abstract
Community members, service users, and people with lived experience, sometimes referred to as “peers,” are increasingly engaged in public health research and practice. The involvement of peers can enhance the acceptability and efficacy of public health interventions, foster trust, and is particularly significant in public health contexts characterized by stigma and criminalization. The involvement of peers in research, such as through community-based participatory research (CBPR) approaches, presents important opportunities for meaningful community engagement. However, the complex challenges associated with research training, capacity building and creating supportive research environments remain underexplored in the literature, particularly for communities that experience social exclusion and marginalization. This study presents a case study of the Overdose Prevention Peer Research Assistant (OPPRA) Project, a CBPR initiative that meaningfully involved harm reduction workers and people who use drugs in research about overdose prevention sites. We describe how inclusion and shared decision-making were accomplished through tailored research training and capacity building with peers and the process of developing supportive research environments that integrate social supports and meet peer research assistants “where they are at.” Further, we outline innovative and collaborative methods for data gathering and analysis developed to involve peers at every step of the research process and share the results of this original research. Results demonstrate the potential for overdose prevention sites to serve as critical points of connection and promote human rights and wellbeing for peer workers, service users, and the broader community.
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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 | 0.047 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".