Transforming global health education during the COVID-19 era: perspectives from a transnational collective of global health students and recent graduates
Bibliographic record
Abstract
on global health (GH) teaching during the COVID-19 pandemic, a group of GH students and recent graduates from around the world convened to discuss our experiences in GH education during multiple global crises. Through weekly meetings over the course of several months, we reflected on the impact the COVID-19 pandemic and broader systemic inequities and injustices in GH education and practice have had on us over the past 2 years. Despite our geographical and disciplinary diversity, our collective experience suggests that while the pandemic provided an opportunity for changing GH education, that opportunity was not seized by most of our institutions. In light of the mounting health crises that loom over our generation, emerging GH professionals have a unique role in critiquing, deconstructing and reconstructing GH education to better address the needs of our time. By using our experiences learning GH during the pandemic as an entry point, and by using this collective as an incubator for dialogue and re-imagination, we offer our insights outlining successes and barriers we have faced with GH and its education and training. Furthermore, we identify autonomous collectives as a potential viable alternative to encourage pluriversality of knowledge and action systems and to move beyond Western universalism that frames most of traditional academia.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".