Master of Science (MS) and Master of Arts (MA) Degrees in Global Health: Applying Interdisciplinary Research Skills to the Study of Globalization-Related Health Disparities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graduate global health education has grown in popularity over the past decade. The Association of Schools and Programs of Public Health has defined global public health competencies for Master of Public Health (MPH) degrees, but there are no similarly established lists of learning outcomes for other types of master’s degrees in global health. The objective of this study was to examine the program goals, curricula, and applied learning requirements for non-MPH master’s degrees in order to understand how global health is being defined and operationalized by these programs. We identified the 14 universities in the United States and Canada offering Master of Science (MS) or Master of Arts (MA) degrees in global health in 2019. Their program descriptions typically emphasize applied research skills, interdisciplinary and multidisciplinary approaches, health disparities, and globalization. Both MS and MA degree pathways use a similar research-oriented core curriculum in which (1) foundational courses introduce the social and environmental determinants of health and global burden of disease trends in the context of globalization, global health ethics, and health systems and policy; (2) a research core develops competencies in biostatistics, epidemiology, and quantitative and qualitative research methods; and (3) a thesis or other written capstone project synthesizes and applies knowledge. Only 4 of the 14 programs require an international field experience, but most encourage applied experiential learning activities. Global health appears to be maturing as an academic discipline, with non-MPH graduate degrees in global health emphasizing similar knowledge areas, research skills, and competencies.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it