Building knowledge broker capacities during the regional research project: lessons learnt from the West African Health Organisation
Bibliographic record
Abstract
The aim of the article was to understand how its project entitled "moving maternal, newborn and child evidence into policy in West Africa" funded under the Canadian initiative has helped the West African Health Organisation to better build its role as a knowledge broker in West Africa. A case study approach was adopted to allow for the validation or otherwise of stated hypotheses using context, actor, process, and outcome data. WAHO as a HPRO has been able through the Moving Maternal Newborn and Child Health Evidence into policy in West Africa project to play the role of KB through strengthening collaboration between policy makers and researchers, generating knowledge for a better understanding of the evidence use environment in the field of maternal and child health, and finally through the implementation of actions to create an enabling environment for the use of evidence to influence health policy and practice. Weaknesses were noted and mostly corrected in the implementation process. The commitment of senior management and other stakeholders, the implementation of the project using the strengths of the institution, the collaboration with regional experts, the provision of additional human resources and the use of evidence in planning project activities facilitate this capacity building. The experience gained enabled the organisation during the COVID-19 pandemic to continue to facilitate the use of evidence for decision making by policy makers in West 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 | 0.121 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".