Research led by people who use drugs: centering the expertise of lived experience
Bibliographic record
Abstract
BACKGROUND: Research collaborations between people who use drugs (PWUD) and researchers are largely underutilized, despite the long history of successful, community-led harm reduction interventions and growing health disparities experienced by PWUD. PWUD play a critical role in identifying emerging issues in the drug market, as well as associated health behaviors and outcomes. As such, PWUD are well positioned to meaningfully participate in all aspects of the research process, including population of research questions, conceptualization of study design, and contextualization of findings. MAIN BODY: We argue PWUD embody unparalleled and current insight to drug use behaviors, including understanding of novel synthetic drug bodies and the dynamics at play in the drug market; they also hold intimate and trusting relationships with other PWUD. This perfectly situates PWUD to collaborate with researchers in investigation of drug use behaviors and development of harm reduction interventions. While PWUD have a history of mistrust with the medical community, community-led harm reduction organizations have earned their trust and are uniquely poised to facilitate research projects. We offer the North Carolina Survivors Union as one such example, having successfully conducted a number of projects with reputable research institutions. We also detail the fallacy of meaningful engagement posed by traditional mechanisms of capturing community voice. As a counter, we detail the framework developed and implemented by the union in hopes it may serve as guidance for other community-led organizations. We also situate research as a mechanism to diversify the job opportunities available to PWUD and offer a real-time example of the integration of these principles into public policy and direct service provision. CONCLUSION: In order to effectively mitigate the risks posed by the fluid and volatile drug market, research collaborations must empower PWUD to play meaningful roles in the entirety of the research process. Historically, the most effective harm reduction interventions have been born of the innovation and heart possessed by PWUD; during the current overdose crisis, there is no reason to believe they will not continue to be.
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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 | 0.053 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.032 | 0.070 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.004 | 0.045 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".