Spanning maternal, newborn and child health (MNCH) and health systems research boundaries: conducive and limiting health systems factors to improving MNCH outcomes in West Africa
Bibliographic record
Abstract
BACKGROUND: Despite improvements over time, West Africa lags behind global as well as sub-Saharan averages in its maternal, newborn and child health (MNCH) outcomes. This is despite the availability of an increasing body of knowledge on interventions that improve such outcomes. Beyond our knowledge of what interventions work, insights are needed on others factors that facilitate or inhibit MNCH outcome improvement. This study aimed to explore health system factors conducive or limiting to MNCH policy and programme implementation and outcomes in West Africa, and how and why they work in context. METHODS: We conducted a mixed methods multi-country case study focusing predominantly, but not exclusively, on the six West African countries (Burkina Faso, Benin, Mali, Senegal, Nigeria and Ghana) of the Innovating for Maternal and Child Health in Africa initiative. Data collection involved non-exhaustive review of grey and published literature, and 48 key informant interviews. We validated our findings and conclusions at two separate multi-stakeholder meetings organised by the West African Health Organization. To guide our data collection and analysis, we developed a unique theoretical framework of the link between health systems and MNCH, in which we conceptualised health systems as the foundations, pillars and roofing of a shelter for MNCH, and context as the ground on which the foundation is laid. RESULTS: A multitude of MNCH policies and interventions were being piloted, researched or implemented at scale in the sub-region, most of which faced multiple interacting conducive and limiting health system factors to effective implementation, as well as contextual challenges. Context acted through its effect on health system factors as well as on the social determinants of health. CONCLUSIONS: To accelerate and sustain improvements in MNCH outcomes in West Africa, an integrated approach to research and practice of simultaneously addressing health systems and contextual factors alongside MNCH service delivery interventions is needed. This requires multi-level, multi-sectoral and multi-stakeholder engagement approaches that span current geographical, language, research and practice community boundaries in West Africa, and effectively link the efforts of actors interested in health systems strengthening with those of actors interested in MNCH outcome improvement.
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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 | 0.039 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| 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".