A model of co-creation: strengthening primary health care (PHC) in Ghana through an innovative “Nyansapo” partnership
Bibliographic record
Abstract
The Africa Health Collaborative (AHC) initiative embarked on a transformative ten-year collaboration with Kwame Nkrumah University of Science and Technology (KNUST) and the University of Toronto (U of T) to co-create continuing education programs geared toward augmenting the proficiency of primary care practitioners in Ghana. While upholding core principles within the AHC framework, emphasizing respect, inclusivity, equity, reciprocity, ethics, dynamism, and stewardship, seven teams of U of T and KNUST faculty engaged in collaborative efforts to design, administer, and evaluate five in-person "short courses" in Ghana on Palliative Care, Quality Improvement for Health Professionals, Prehospital Emergency Care, Community Emergency Care, and Emergency Preparedness and Response to Epidemic-Prone Diseases to approximately 100 Ghanaian primary care professionals. This paper describes a model of co-creation, highlights lessons learned from a robust evaluation process, and proposes that this co-creation model can strengthen primary health care in Ghana and ultimately transform health systems in Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".