Decolonizing Global Health Partnerships: Exploring International Partnerships in Health Professions Education Through a Postcolonial Lens
Bibliographic record
Abstract
The education of health care professionals plays an important role in addressing global population health disparities and strengthening health systems. One avenue to address the global shortage of health professions is to invest in health professions education globally through international academic collaborations, particularly partnerships between high-income countries (HICs) and low- or middle-income countries (LMICs). While global health partnerships have intensified in the first two decades of the 21st century, only a fraction are described in published literature. Additionally, published accounts of partnerships seldom take into account the perspective and experiences of the LMIC partner. Furthermore, partnerships are rarely examined from a perspective that acknowledges unequal relations resulting from historical colonial ruling and neocolonial globalization.The purpose of this study was to problematize HIC and LMIC international partnerships in health professions education through a postcolonial theory lens. The aims of this study were: 1) To explore moments of success and challenges experienced by partners during the everyday processes that shape the partnership; and, 2) To explore these partnerships taking into account power imbalances and unequal relations resulting from historic colonial ruling and neocolonial globalization. The postcolonial theory concepts of neocolonialism, globalization, global citizenship, and knowledge and power were operationalized using critical qualitative methodology. Ninteen participants (ten from HIC and nine from LMIC) from 13 different partnerships were interviewed using a semi-structured approach using video-conferencing. Data were analysed using inductive, deductive and abductive reasoning. This study offers an augmented view of partnerships summarized by three key findings. First, partnerships are greatly reliant on human relationships which are enacted by highly committed partners that embody an anticolonial ethos. Secondly, a deeper appreciation of the partnership process is required in order to value alternative outcomes, celebrate achievements, and reinvigorate partners. Third, in order to decolonize global partnerships in health professions education between HICs and LMICs, a deep understanding of power dynamics and neocolonial relations is needed. Valuing people, appreciating processes and contextual factors, and acknowledging power relations as well as an engagement with complexity are required for current and future partnerships to contribute to social justice and decolonization.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.036 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".