Disinvesting in the future leadership of global health has already begun: What can we do about it?
Bibliographic record
Abstract
Following the abrupt and significant funding cuts by the U.S. and increasing retreat by high-income countries from development aid for health, global health as a field requires reimagining and urgent solution building by all parties involved. In this essay, we aim to draw attention to an important and urgent challenge that deeply affects our collective future: the destruction of global health training opportunities and the weakening of future global health leadership. If we do not approach this challenge with a sense of urgency, global health research and training face irreversible shifts, weakening global preparedness to face future pandemics, address climate crisis, and achieve global goals such as universal health coverage or health for all. We outline existing best practices that we can build on and pathways to build better approaches in global health training.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".