The WHO simulation initiative: improving global health partnerships
Bibliographic record
Abstract
Addressing the problem Health is unequivocally global. Increasing numbers of students and young professionals in health-allied fields are looking to collaborate and work beyond the confines of their national borders. National governments are committed to improving the health of people across the world, as outlined in an example document “Health is Global: a UK Government strategy 2008-2013” [1], which considers the benefits of health improvement to be reciprocal to all parties involved. We therefore need appropriately trained human resources to deliver improvements in global public health. At present, few opportunities exist in the undergraduate medical curriculum to formally develop global public health skills. Medical schools and universities are increasingly establishing modules and indeed degrees at both the undergraduate and postgraduate level focused on global health. These courses are for the most part based on a formula of lectures, tutorials and a research project, and do not necessarily expose students to the skills required for global health diplomacy. The recent WHO Simulation Initiative, outlined in this brief report, looks to redress this gap by providing delegates with an innovative environment to discuss topics of global health importance, gain confidence in public speaking and develop the negotiation skills required to affect change at an institutional level. Simulation based education is a powerful learning tool. The recent Commission on the Education of Health Professionals for the 21 Century—a global independent initiative [2]—was launched in January 2010 to review medical education across the globe with the aim of “transforming education to strengthen health systems in an interdependent world”. A key recommendation of the commission was for education to adopt transformative learning. This would involve developing the current model built on acquiring knowledge, skills and values, to one where the end goal was furthering leadership attributes
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".