Using the COVID-19 pandemic to reimagine global health teaching in high-income countries
Bibliographic record
Abstract
The COVID-19 pandemic has changed how we live, work and communicate. Global health teaching is no exception. Across universities, professors like us have had to quickly redesign our courses, and deliver them virtually, even as the pandemic continues to bring new challenges every day. Out of that struggle, new learning opportunities have emerged. This editorial, coauthored by 20 professors in seven high-income countries (HICs), aims to synthesise our learnings and insights from over 25 courses we taught (or are currently teaching).1 We acknowledge upfront that our insights might not transfer to global health teaching in all contexts, especially in settings where the digital divide is worsening educational inequities. We hope to learn from similar articles on how our colleagues in low/middle-income countries (LMICs) have adapted and innovated with their teaching during this crisis. Our collective experience suggests that despite the pandemic chaos and fatigue, global health teaching can be improved (box 1) by using COVID-19 as a teachable moment to focus on equity and human rights as a central theme, and by integrating anti-racism and anti-oppression as core content and orientation in our curriculum. The online format allows instructors to centre voices from the Global South, Indigenous scholars, and individuals with lived experience of oppression and resilience. Remote teaching also helps us reach wider and diverse audiences, including groups that may not be enrolled in traditional degree programmes. Learning from COVID-19, which is widening disparities within and across countries, global health teaching must educate students to address health disparities wherever they occur, not just in LMICs. While the online format offers many challenges, we believe there are ways to increase student engagement and reduce fatigue (box 2). Box 1 ### Adapting content and scope of global health teaching during the pandemic
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".