The ethics of community-based research with people who use drugs: results of a scoping review
Bibliographic record
Abstract
BACKGROUND: Drug user networks and community-based organizations advocate for greater, meaningful involvement of people with lived experience of drug use in research, programs and services, and policy initiatives. Community-based approaches to research provide an opportunity to engage people who use drugs in all stages of the research process. Conducting community-based participatory research (CBPR) with people who use drugs has its own ethical challenges that are not necessarily acknowledged or supported by institutional ethics review boards. We conducted a scoping review to identify ethical issues in CBPR with people who use drugs that were documented in peer-reviewed and grey literature. METHODS: The search strategy focused on three areas; community-based research, ethical issues, and drug use. Searches of five academic databases were conducted in addition to a grey literature search, hand-searching, and consultation with organizational partners and key stakeholders. Peer reviewed literature and community reports published in English between 1985 and 2013 were included, with initial screening conducted by two reviewers. RESULTS: The search strategy produced a total of 874 references. Twenty-five references met the inclusion criteria and were included in our thematic analysis. Five areas were identified as important to the ethics of CBPR with people who use drugs: 1) participant compensation, 2) drug user perspectives on CBPR, 3) peer recruitment and representation in CBPR, 4) capacity building, and 5) participation and inclusion in CBPR. CONCLUSIONS: We critically discuss implications of the emerging research in this field and provide suggestions for future research and practice.
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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 | 0.197 | 0.367 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.042 | 0.050 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 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; 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".