Meaningful engagement of people living with HIV who use drugs: methodology for the design of a Peer Research Associate (PRA) hiring model
Bibliographic record
Abstract
Abstract Background Community-based HIV, harm reduction, and addiction research increasingly involve members of affected communities as Peer Research Associates (PRAs)â individuals with common experiences to the participant population (e.g. people who use drugs, people living with HIV [PLHIV]). However, there is a paucity of literature detailing the operationalization of PRA hiring and thus limited understanding regarding how affected communities can be meaningfully involved through low-barrier engagement in paid positions within community-based participatory research (CBPR) projects. We aim to address this gap by describing a low-threshold PRA hiring process. Results In 2012, the BC Centre for Excellence in HIV/AIDS and the Dr. Peter AIDS Foundation collaborated to develop a mixed-method CBPR project evaluating the effectiveness of the Dr. Peter Centre (DPC)â an integrative HIV care facility in Vancouver, Canada. A primary objective of the study was to assess the impact of DPC services among clients who have a history of illicit drug use. In keeping with CBPR principles, affected populations, community-based organizations, and key stakeholders guided the development and dissemination of a low-barrier PRA hiring process to meaningfully engage affected communities (e.g. PLHIV who have a history of illicit drug use) in all aspects of the research project. The hiring model was implemented in a number of stages, including (1) the establishment of a hiring team; (2) the development and dissemination of the job posting; (3) interviewing applicants; and (4) the selection of participants. The hiring model presented in this paper demonstrates the benefits of hiring vulnerable PLHIV who use drugs as PRAs in community-based research. Conclusions The provision of low-barrier access to meaningful research employment described herein attempts to engage affected communities beyond tokenistic involvement in research. Our hiring model was successful at engaging five PRAs over a 2-year period and fostered opportunities for future paid employment or volunteer opportunities through ongoing collaboration between PRAs and a diverse range of stakeholders working in HIV/AIDS and addictions. Additionally, this model has the potential to be used across a range of studies and community-based settings interested in meaningfully engaging communities in all stages of the research process.
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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 | 0.186 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".