Online Education for Public Health Capacity Building in Low- to Middle-Income Countries
Bibliographic record
Abstract
People’s Open Access Education Initiative (Peoples-uni, http://peoples-uni.org) aims to contribute to improvements in the health of populations in low- to middle-income countries by building public health capacity via e-learning at affordable cost. We describe experience over nine years of the initiative, including the development and delivery of a Master of Public Health (MPH) programme in public health and collaboration with a UK University. Courses rely on Open Educational Resources and volunteer tutors from over 50 countries to date. During 18 semesters since 2008, 1619 students from 92 countries (71% from Africa) enrolled. Of 128 students accepted on an MPH programme accredited by a UK University, 94 earned an MPH (73%) and a further 18 (14%) achieved a postgraduate diploma or certificate. Other developments include continuing involvement with Alumni, and a sister site for Open Online Courses to include topics not often found in MPH courses. We offer insights for further development of this and similar online capacity building programmes within low-resource environments. Our experience shows the feasibility of affordable, high quality online education and that there is scope for accelerating capacity building programmes through partnerships with higher education institutions and health(care) organisations.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".