Bibliographic record
Abstract
Meaningful engagement and partnerships with people who use drugs are essential to conducting research that is relevant and impactful in supporting desired outcomes of drug consumption as well as reducing drug-related harms of overdose and COVID-19. Community-based participatory research is a key strategy for engaging communities in research that directly affects their lives. While there are growing descriptions of community-based participatory research with people who use drugs and identification of key principles for conducting research, there is a gap in relation to models and frameworks to guide research partnerships with people who use drugs. The purpose of this paper is to provide a framework for research partnerships between people who use drugs and academic researchers, collaboratively developed and implemented as part of an evaluation of a provincial prescribed safer supply initiative introduced during dual public health emergencies (overdose and COVID-19) in British Columbia, Canada. The framework shifts from having researchers choose among multiple models (advisory, partnership and employment) to incorporating multiple roles within an overall community-based participatory research approach. Advocacy by and for drug users was identified as a key role and reason for engaging in research. Overall, both academic researchers and Peer Research Associates benefited within this collaborative partnerships approach. Each offered their expertise, creating opportunities for omni-directional learning and enhancing the research. The shift from fixed models to flexible roles allows for a range of involvement that accommodates varying time, energy and resources. Facilitators of involvement include development of trust and partnering with networks of people who use drugs, equitable pay, a graduate-level research assistant dedicated to ongoing orientation and communication, technical supports as well as fluidity in roles and opportunities. Key challenges included working in geographically dispersed locations, maintaining contact and connection over the course of the project and ensuring ongoing sustainable but flexible employment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".