Advancing Equity-Centered Research in Global Health: Exploring the Legacies of Colonialism in Global Health Research
Bibliographic record
Abstract
Global health, as a field of research and practice, prioritizes some of the most pressing social and environmental challenges of our time including the health effects of climate change, response to emerging and re-emerging infectious diseases, and health inequities across the world. However, Western-dominated construction of health and disease diagnosis, as well as colonial ideologies within the field continues to reverberate in many circles. This research aims to explore the legacies of colonialism in global health research and investigate the methods and strategies used by researchers to address these legacies and advance equity. In-depth semi-structured interviews were conducted with participants (n=13) who self-identified as global health researchers from major Canadian and low-and-middle-income country (LMIC) universities (Afghanistan, Zambia, Kenya, Bangladesh, Ghana, and Indonesia). Factors such as gender, role, expertise, discipline, and geographic location were considered to ensure maximum variation in data collection. Six (6) LMIC participants and 7 Canadian participants were successfully recruited. Participants gave verbal consent to use a cloud-based video communications app, Zoom, to conduct interviews. Findings show that funding mechanisms, hierarchies, power imbalances, privilege and power, and local manifestations are some major legacies of colonialism in global health research. These legacies influence current research priorities, methodologies, and knowledge production mechanisms, as well as partnerships and collaborations in global health research. The methods and strategies used by researchers to address these colonial legacies comprise using community-based participatory action research (CBPAR), integrated knowledge-translation approach (IKT), capacity building, the Canadian Coalition of Global Health Research (CCGHR) Principles of Global Health Research framework, and other arts-based methods. In conclusion, it is important for researchers to become responsive to local realities while thinking critically about historical narratives to understand the political, social, and economic contexts in which global health research, programs, and partnerships occur. Decolonizing global health is difficult because the legacies of colonialism are deeply embedded with systems of knowledge production, global governance structures, and economic relationships between nations. However, we can address these issues at the institutional and individual levels by adopting and adapting the CCGHR Principles of Global Health Research, capacity building, and promoting South-South collaborations. This will ultimately lead to more equitable partnerships and collaborations and move towards decolonizing global health research.
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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 | 0.174 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.029 | 0.093 |
| Scholarly communication | 0.037 | 0.023 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".