Fostering Global Research Collaborations: An Update on Duke-NUS Medical School, the Duke University and National University of Singapore Partnership
Bibliographic record
Abstract
PROBLEM: Navigating the complexities of international research collaborations is a challenge. This article provides a detailed examination of the international collaboration between Duke University and the National University of Singapore to establish the Duke-NUS Medical School. It explores the evolution and impact of the partnership, focusing on outcomes, knowledge advancement, and the dynamics of international collaborations in academic medicine. APPROACH: The partnership began in 2005 and applies a collaborative approach, including aligning research foci with Singapore's national health priorities, the formation of an academic medical center, faculty exchanges, joint funding for pilot research, and pooling of expertise, diverse and multiethnic data, and samples. OUTCOMES: The collaboration has led to educational and research advancements, including significant contributions to global health, such as the development of the first U.S. Food and Drug Administration-approved SARS-CoV-2 antigen test and a nasal COVID-19 vaccine candidate. Additionally, it has enhanced academic medicine capabilities within Singapore by transforming teaching hospitals into a fully integrated academic medical center. This experience suggests the following toward advancing the partnership: (1) agreement on and revisiting of the shared vision of the partnership by institutional leaders, (2) middle- and end-period reviews within multiyear funding cycles from local ministries, (3) faculty engagement through collaborative resources and spaces, and (4) similar first languages and health systems of the partners. NEXT STEPS: The Duke-NUS Medical School partnership aims to expand its research areas to address more global health challenges, such as the impact of climate change on health and the advancement of precision medicine. This article offers valuable insights for understanding the dynamics, benefits, and challenges of international collaborations in academic medicine.
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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 | 0.056 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".