Decolonising ideas of healing in medical education
Bibliographic record
Abstract
The legacy of colonial rule has permeated into all aspects of life and contributed to healthcare inequity. In response to the increased interest in social justice, medical educators are thinking of ways to decolonise education and produce doctors who can meet the complex needs of diverse populations. This paper aims to explore decolonising ideas of healing within medical education following recent events including the University College London Medical School’s Decolonising the Medical Curriculum public engagement event, the Wellcome Collection ’s Ayurvedic Man: Encounters with Indian Medicine exhibition and its symposium on Decolonising Health, SOAS University of London’s Applying a Decolonial Lens to Research Structures, Norms and Practices in Higher Education Institutions and University College London Anthropology Department’s Flourishing Diversity Series. We investigate implications of ‘recentring’ displaced indigenous healing systems, medical pluralism and highlight the concept of cultural humility in medical training, which while challenging, may benefit patients. From a global health perspective, climate change debates and associated civil protests around the issues resonate with indigenous ideas of planetary health , which focus on the harmonious interconnection of the planet, the environment and human beings. Finally, we look further at its implications in clinical practice, addressing the background of inequality in healthcare among the BAME (Black, Asian and minority ethnic) populations, intersectionality and an increasing recognition of the role of inter-generational trauma originating from the legacy of slavery. By analysing these theories and conversations that challenge the biomedical view of health, we conclude that encouraging healthcare educators and professionals to adopt a ‘ decolonising attitude ’ can address the complex power imbalances in health and further improve person-centred care.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.110 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".