Decolonizing global health research: experiences from the women in health and their economic, equity and livelihood statuses during emergency preparedness and response (WHEELER) study
Bibliographic record
Abstract
Decolonizing global health research involves rethinking power structures and research collaboration to prioritize the voices and experiences of communities that have been historically marginalized. Cross-sectoral and cross-regional partnerships based on reciprocity, trust, and transparency can be facilitated by decolonized research frameworks. To address global health issues in a way that is inclusive, context-specific, and genuinely advantageous to all parties involved, especially communities most impacted by health disparities, the ethics behind this change is imperative. We applied a decolonizing health research approach to the Women in Health and their Economic, Equity, and Livelihood Statuses During Emergency Preparedness and Response (WHEELER) study to explore the connections between gender, health, and economic equity in times of crisis in two counties in Kenya. This paper outlines seven key dimensions that guided the WHEELER study in transforming power dynamics in research, decolonizing research processes, and fostering equitable partnerships. The study employed participatory methodologies, integrating the Equity in Partnership instrument from the Canadian Coalition for Global Health Research (CCGHR) Principles, human-centered design (HCD), and gender-based analysis to ensure inclusivity, gender sensitivity, and active participation. The participatory approach was implemented through the engagement of a Community Research Advisory Group (CRAG) and a Local Advisory Board (LAB). Utilizing mixed methods and community-engaged processes, the study fostered reciprocal growth, learning, and change among local health officials and research teams. Our participatory approach fostered strengthened engagement, promoted shared decision-making, and enhanced the sense of ownership among policy implementers throughout the research process.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.029 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".