What can an Interprofessional Global Health Course with a Focus on Decolonization Bring to Students? A Qualitative Study
Bibliographic record
Abstract
Many voices have called for dismantling the colonial legacies that permeate healthcare systems. McGill’s Interprofessional Global Health Course 2021 online edition adopted the theme of decolonizing global health. This study aimed to understand the perspectives of students enrolled in this course on a) colonial patterns embedded in global health, and b) future actions that students can take to decolonize global health. A qualitative descriptive methodology was employed. The study population included students who completed the course during the Winter 2021 semester. Following the last session, students were asked to answer four open-ended questions. The answers were analyzed thematically using inductive and deductive coding. Eighty-one of the 105 students registered for the course answered the questions and data saturation was reached after analyzing 24 answer sheets. Two themes emerged: the course informed students about the role of colonial legacies in shaping global health systems and the course helped students understand global health decolonization and plan to take relevant actions. To promote global health decolonization, future healthcare workers need to be sensitized to the ongoing impacts of colonialism. Healthcare education can serve this function through the examination and modification of curricula, but also through the employment of innovative educational approaches that help students reflect on their professional roles and responsibilities towards global health 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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".