The Impact of a Model Partnership in a Medical Postgraduate Program in North–South and South—South Collaboration on Trainee Retention, Program Sustainability and Regional Collaboration
Bibliographic record
Abstract
North-South educational partnerships can potentially alleviate the scarcity of health work force in the South. A model program with the objectives of sustainability, high trainee retention, quality education, and capacity building is the goal of many similar programs. To achieve these goals a program of postgraduate clinical specialty training was implemented, between the University of Bergen, Norway and three Universities in Africa and one medical school in India between 2008 to 2014. This partnership program aimed at educating physicians from the South to specialize in various medical field. The goal was that the trained physicians would be educators in their respective countries. The program participants were 58 medical doctors.At the end of the program we conducted an evaluation survey involving program participants and coordinators. Twenty-eight physicians (48%) responded to the survey. The average program evaluation score by the physicians was 4.5 (out of a maximum of 5) with a range of 3.2 to 5. Out of the 12 program coordinators 9 (75%) responded to the survey. Their average score was 4.5 with a range of 4 to 4.9. By the time the survey was conducted, 49 of the 58 (84.5%) program participants had completed the program successfully and 47 of the 49 (95.9 %) were working in their own countries.In conclusion, the partnership program was effective in capacity building in the development of the human health workforce in Africa. We have observed a high retention rate, good quality education, a sustainable program and the model can easily be reproducible.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".