New Academic Partnerships in Global Health: Innovations at Mount Sinai School of Medicine
Bibliographic record
Abstract
Global health has become an increasingly important focus of education, research, and clinical service in North American universities and academic health centers. Today there are at least 49 academically based global health programs in the United States and Canada, as compared with only one in 1999. A new academic society, the Consortium of Universities for Global Health, was established in 2008 and has grown significantly. This sharp expansion reflects convergence of 3 factors: (1) rapidly growing student and faculty interest in global health; (2) growing realization-powerfully catalyzed by the acquired immune deficiency syndrome epidemic, the emergence of other new infections, climate change, and globalization-that health problems are interconnected, cross national borders, and are global in nature; and (3) rapid expansion in resources for global health. This article examines the evolution of the concept of global health and describes the driving forces that have accelerated interest in the field. It traces the development of global health programs in academic health centers in the United States. It presents a blueprint for a new school-wide global health program at Mount Sinai School of Medicine. The mission of that program, Mount Sinai Global Health, is to enhance global health as an academic field of study within the Mount Sinai community and to improve the health of people around the world. Mount Sinai Global Health is uniting and building synergies among strong, existing global health programs within Mount Sinai; it is training the next generation of physicians and health scientists to be leaders in global health; it is making novel discoveries that translate into blueprints for improving health worldwide; and it builds on Mount Sinai's long and proud tradition of providing medical and surgical care in places where need is great and resources few.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".