NextGenU.org: the first globally free degree, a Master's in Public Health
Bibliographic record
Abstract
Abstract Background NextGenU.org and partners have offered (since 2012) free, accredited health sciences courses, with registrants in 192 (of 193) countries. We have now developed the world's first free, accredited degree: The Master of Public Health (MPH), including registration, teaching, testing, and co-granting of a degree. To this end, we continue collaborating with leading public health organizations and educational institutions. Objectives Our objective is to improve the health of the underserved, remove barriers to diversifying the health professional workforce, and promote understanding of global/population health. The MPH provides tested and validated interprofessional education in an ongoing timeframe. Institutions add to our MPH via qualified mentors to guide the competency-based mentored activities. Universities may use any/all parts and offer the courses/degrees to students for free or for the school's cost of mentoring. Knowledge acquisition continues to be computer-based via resources from academic, government, professional society, and peer-reviewed journal sources; local and remote peer and mentored activities are included. Results We have tested and published on this free model in North American public health, medical, and undergrad students, and in community health workers and primary care physicians in Kenya and India, showing as much knowledge gain and greater student satisfaction than with traditional courses. Peer-reviewed publications on NextGenU.org's efficacy are available at https://nextgenu.org/static/publications.html. All our trainings are reviewed/endorsed by our global academic, governmental and professional partners. Conclusions Our research shows the MPH creates local opportunities for health workers in their communities, reducing brain drain and exodus of health workers. We hope that conference attendees discovering these educational innovations translate our findings into action, distributing this resource to their institutions and countries. Key messages Our free MPH enables would-be and current health professionals and other learners globally to have equal access to first-class learning resources, without economic or geographic barriers. Because NextGenU.org is able to freely offer these first-class resources to all, our MPH can—and will—change the face of global health through creating worldwide communities of practice.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.214 | 0.075 |
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".