'Walking Along Beside the Researcher': How Canadian REBs/IRBs ar eResponding to the Needs of Community-Based Participatory Research
Bibliographic record
Abstract
research ethics boards and institutional review boards (REBs/IRBs) have been criticized for relying on conceptions of research that privilege biomedical, clinical, and experimental designs, and for penalizing research that deviates from this model. Studies that use a community-based participatory research (CBPR) design have been identified as particularly challenging to navigate through existing ethics review frameworks. However, the voices of REB/IRB members and staff have been largely absent in this debate. The objective of this article is to explore the perspectives of members of Canadian university-based REBs/IRBs regarding their capacity to review CBPR protocols. We present findings from interviews with 24 Canadian REB/IRB members, staff, and other key informants. Participants were asked to describe and contrast their experiences reviewing studies using CBPR and mainstream approaches. Contrary to the perception that REBs/IRBs are inflexible and unresponsive, participants described their attempts to dialogue and negotiate with researchers and to provide guidance. Overall, these Canadian REBs/IRBs demonstrated a more complex understanding of CBPR than is typically characterized in the literature. Finally, we situate our findings within literature on relational ethics and explore the possibility of researchers and REBs/IRBs working collaboratively to find solutions to unique ethical tensions in CBPR
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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 | 0.070 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.088 | 0.050 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".