Rural community-based participatory research with families of people who use drugs: key considerations from a multi-provincial research partnership
Bibliographic record
Abstract
BACKGROUND: North America continues to contend with an unregulated drug crisis that is impacting communities of all sizes. Community-based participatory research that meaningfully engages people who use drugs, families, and their wider communities is one way of advancing social justice and improving population health. As more community-academic partnerships are formed in this space, some organizations have launched guidelines and considerations for engaging in community-based participatory research (CBPR). However, to our knowledge, none provide guidance for engaging in CBPR with people who use drugs and their families in rural settings. MAIN BODY: This paper presents insights gained from our experiences collaborating to conduct CBPR with families providing unpaid support for people who use drugs in rural Canada. Key considerations are thematically organized in four sections: Dreaming (Building the team and setting a vision), Designing (Key definitions, budget and ethical consideration), Doing (Bringing research to life), and Disseminating (Moving research into action). CONCLUSIONS: By building on existing principles and guidelines for working with PWUD and their families, these considerations will be a valuable resource for other partnerships seeking to engage in community-based participatory substance use research in rural 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 | 0.205 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".