Do Research Ethics Guidelines Promote Equitable Power Relationships Between Low-and-Middle-Income Country Communities and High-Income Country Researchers?
Bibliographic record
Abstract
Background: Increasingly, Black, Indigenous, and People of Colour scholars are calling for global health to be decolonized—to redress the power imbalances in global health. Establishing equitable power relationships between low-and-middle-income country (LMIC) actors and high-income country (HIC) actors has been proposed as a way to decolonize global health. Community engagement (CE) is recognized as an aspect of global health research in need of decolonizing. It has yet to be examined, however, whether research ethics guidelines that contain CE guidance promoted the establishment of equitable power relationships between LMIC communities and HIC researchers in connection with CE in global health research. Purpose and Methods: To address this gap, I used qualitative content analysis to examine whether 19 currently circulating research ethics guidelines—8 international, 10 LMIC, and one LMIC community level guideline, promoted the establishment of equitable power relationships between LMIC communities and HIC researchers in connection with CE in global health research. To accomplish this, I interrogated my data using the community-based participatory research (CBPR) paradigm because it theorizes how to establish equitable power relationships between communities and researchers. I also took steps to practice critical allyship to decolonize my research process. Findings: Overall, my findings demonstrated that the 19 research ethics guidelines analyzed for this study did not strongly promote the establishment of equitable power relationships between LMIC communities and HIC researchers in close alignment with the CBPR paradigm. None of the research ethics guidelines contained closely aligned core domains in connection with all, or even most, of the CBPR paradigm’s 13 core domains of community-researcher power relationships. My findings also demonstrated that international research ethics guidelines contained more core domains that closely aligned with the CBPR paradigm. Conclusions: As a step towards promoting the establishment of equitable power relationships between LMIC communities and HIC researchers, I propose HIC researchers should build their capacity to be critical allies to LMIC communities in the context of global health research and I propose what doing so could entail. I also propose how research ethics guidelines could contribute to building the capacity of HIC researchers to become critical allies.
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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 | 0.301 | 0.494 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 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; 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".