Establishing a community-based participatory research partnership among people who use drugs in Ottawa: the PROUD cohort study
Bibliographic record
Abstract
BACKGROUND: Grounded in a community-based participatory research (CBPR) framework, the PROUD (Participatory Research in Ottawa: Understanding Drugs) Study aims to better understand HIV risk and prevalence among people who use drugs in Ottawa, Ontario. The purpose of this paper is to describe the establishment of the PROUD research partnership. METHODS: PROUD relies on peers' expertise stemming from their lived experience with drug use to guide all aspects of this CBPR project. A Community Advisory Committee (CAC), comprised of eight people with lived experience, three allies and three ex-officio members, has been meeting since May 2012 to oversee all aspects of the project. Eleven medical students from the University of Ottawa were recruited to work alongside the committee. Training was provided on CBPR; HIV and harm reduction; and administering HIV point-of-care (POC) tests so that the CAC can play a key role in research design, data collection, analysis, and knowledge translation activities. RESULTS: From March-December 2013, the study enrolled 858 participants who use drugs (defined as anyone who has injected or smoked drugs other than marijuana in the last 12 months) into a prospective cohort study. Participants completed a one-time questionnaire administered by a trained peer or medical student, who then administered an HIV POC test. Recruitment, interviews and testing occurred in both the fixed research site and various community settings across Ottawa. With consent, prospective follow-up will occur through linkages to health care records available through the Institute for Clinical and Evaluation Sciences. CONCLUSION: The PROUD Study meaningfully engaged the communities of people who use drugs in Ottawa through the formation of the CAC, the training of peers as community-based researchers, and integrated KTE throughout the research project. This project successfully supported skill development across the team and empowered people with drug use experience to take on leadership roles, ensuring that this research process will promote change at the local level. The CBPR methods developed in this study provide important insights for future research projects with people who use drugs in other 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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".