Ethical Issues in Conducting Community-Based Participatory Research: A Narrative Review of the Literature
Bibliographic record
Abstract
Community-based participatory research (CBPR) is a methodology increasingly used within the social sciences. CBPR is an umbrella term that encompasses a variety of research methodologies, including participatory research, participatory action research, feminist participatory research, action research, and collaborative inquiry. At its core, they share five key attributes: (i) community as a unit of identity; (ii) an approach for the vulnerable and marginalized; (iii) collaboration and equal partnership throughout the entire research process; (iv) an emergent, flexible, and iterative process; and (v) the research process is geared toward social action. While there is no shortage of literature that highlights the benefits and potential of CBPR, relatively little discussion exists on the ethical issues associated with the methodology. In particular, current gaps within the literature include ethical guidance in (i) balancing community values, needs, and identity with those of the individual; (ii) negotiating power dynamics and relationships; (iii) working with stigmatized populations; (iv) negotiating conflicting ethical requirements and expectations from Institutional Review Boards (IRBs); and (v) facilitating social action emerging from the findings. For CBPR’s commendable goals and potential to be realized, it is necessary to have a more fulsome discussion of the ethical issues encountered while implementing a CBPR study. Further, a lack of awareness and critical reflection on such ethical considerations may perpetuate the very same problems this methodology seeks to address, namely, inequality, oppression, and marginalization. The purpose of this article is to provide a narrative review of the literature that identifies ethical issues that may arise from conducting CBPR studies, and the recommendations by researchers to mitigate such challenges.
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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 | 0.085 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".