Promoting research to improve maternal, neonatal, infant and adolescent health in West Africa: the role of the West African Health Organisation
Bibliographic record
Abstract
West Africa has adopted numerous strategies to counter maternal and infant mortality, provides national maternal and infant health programmes, and hosts many active technical and financial partners and non-governmental organisations. Despite this, maternal and infant morbidity and mortality indicators are still very high. In this commentary, internal actors and officials of the West African Health Organisation (WAHO) examine the regional organisation's role in promoting research as a tool for strengthening maternal and infant health in West Africa.As a specialised institution of the Economic Community of West African States (ECOWAS) responsible for health issues, WAHO's mission is to provide the sub-region's population with the highest possible health standards by harmonising Member States' policies, resource pooling, and cooperation among Member States and third countries to collectively and strategically combat the region's health problems. To achieve this, WAHO's main intervention strategy is that of facilitation, as this encourages the generation and use of evidence to inform decision-making and reinforce practice.WAHO's analysis of interventions since 2000 showed that it had effected some changes in research governance, management and funding, as well as in individual and institutional capacity building, research dissemination, collaboration and exchanges between the various stakeholders. It also revealed several challenges such as process ownership, member countries' commitment, weak individual and institutional capacity, mobilisation, and stakeholder commitment. To better strengthen evidence-based decision-making, in 2016, WAHO created a unique programme aimed at improving the production, dissemination and use of research information and results in health programme planning to ultimately improve population health.While WAHO's experiences to date demonstrate how a regional health institution can integrate research promotion into the fight against maternal and infant mortality, the challenges the organisation has encountered also demonstrate the importance of cohesion among actors promoting such an initiative, the importance of leadership and commitment among member country actors steering the process, and the need for collaboration and coordination among all partners in member countries and in the region.
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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 | 0.074 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".