Nottingham Trent University and Makerere University School of Public Health partnership: experiences of co-learning and supporting the healthcare system in Uganda
Bibliographic record
Abstract
Partnerships between developed and developing country institutions are increasingly becoming important in addressing contemporary global health challenges faced by health systems. Inter-university health collaboration such as the Nottingham Trent University (UK) and Makerere University School of Public Health (Uganda) partnership provide opportunities for working together in training, research and service delivery while strengthening health systems. This paper shares the experiences, achievements and opportunities of this partnership in co-learning and supporting the health system in Uganda. This includes a project being implemented to strengthen the training, supervision and motivation of community health workers in rural Uganda. Training and research are a key focus of the partnership and have involved both staff and students of both institutions including guest lectures, seminars and conference presentations. The partnership's collaboration with stakeholders such as the Ministry of Health (Uganda) and local health authorities has ensured participation necessary in supporting implementation of sustainable interventions. The partnership uses several channels such as email, telephone, Skype, Dropbox and WhatsApp which have been useful in maintaining constant and effective communication. The challenges faced by the partnership include lack of funding to support student mobility, and varying academic schedules of the two institutions. The experiences and prospects of this growing partnership can inform other collaborations in similar settings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".