Participant, peer and PEEP: considerations and strategies for involving people who have used illicit substances as assistants and advisors in research
Bibliographic record
Abstract
BACKGROUND: The Peer Engagement and Evaluation Project (PEEP) aimed to engage, inspire, and learn from peer leaders who represented voices of people who use or have used illicit substances, through active membership on the 'Peeps' research team. Given the lack of critical reflection in the literature about the process of engaging people who have used illicit substances in participatory and community-based research processes, we provide a detailed description of how one project, PEEP, engaged peers in a province-wide research project. METHODS: By applying the Peer Engagement Process Evaluation Framework, we critically analyze the intentions, strategies employed, and outcomes of the process utilized in the PEEP project and discuss the implications for capacity building and empowerment among the peer researchers. This process included: the formation of the PEEP team; capacity building; peer-facilitated data collection; collaborative data analysis; and, strengths-based approach to outputs. RESULTS: Several lessons were learned from applying the Peer Engagement Process Evaluation Framework to the PEEP process. These lessons fall into themes of: recruiting and hiring; fair compensation; role and project expectations; communication; connection and collaboration; mentorship; and peer-facilitated research. CONCLUSION: This project offers a unique approach to engaging people who use illicit substances and demonstrates how participation is an important endeavor that improves the relevance, capacity, and quality of research. Lessons learned in this project can be applied to future community-based research with people who use illicit substances or other marginalized groups and/or participatory settings.
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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 | 0.457 | 0.468 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.037 | 0.030 |
| Scholarly communication | 0.021 | 0.041 |
| Open science | 0.009 | 0.046 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".