Health policy and systems research capacity development to support maternal, new-born, child and adolescent health in West and Central Africa
Bibliographic record
Abstract
Objectives: To examine how and why a South-South capacity development and networking program for leadership, research, practice and advocacy on maternal new-born, child and adolescent health and health policy and systems strengthening in West Africa and Cameroon worked and identify lessons for low- and middle-income countries. Design: Single qualitative case study drawing on data from document review, observations, key informant interviews and a deliberative workshop. Ethics approval for primary data collection was obtained from the Ghana Health Service Ethical Review Committee (GHS-ERC 012/10/18). Setting: West Africa and Cameroon. Participants: Researchers, policy and programme managers and frontline health workers. Interventions: Networking and capacity development. Results: The programme made good progress in implementing many but not all planned capacity development and networking activities. The opportunity to network with other organisations and individuals and across countries, disciplines, and languages as well as to learn, to develop skills, and obtain mentorship support, were considered valuable benefits of the partnership. Human and financial resource constraints meant that not all planned interventions could be implemented. Conclusions: Lessons for health policy and systems research capacity building in LMIC include the potential of South-South partnerships, the need for dedicated resources, the potential of Sub-regional health organizations to support capacity building and recognition that each effort builds on preceding efforts of others, and that it is important to explore and understand where the energy and momentum for change lies. Funding: The work described here has been funded by IDRC Canada under research grant # 108237 "West and Central African partnership for maternal, new-born, child and adolescent health research."
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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 | 0.052 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".