The ethics of community-based research with people who use drugs: results of a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.116 | 0.577 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.029 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it