Challenges and facilitators to evidence-based decision-making for maternal and child health in Mozambique: district, municipal and national case studies
Bibliographic record
Abstract
BACKGROUND: The need for evidence-based decision-making in the health sector is well understood in the global health community. Yet, gaps persist between the availability of evidence and the use of that evidence. Most research on evidence-based decision-making has been carried out in higher-income countries, and most studies look at policy-making rather than decision-making more broadly. We conducted this study to address these gaps and to identify challenges and facilitators to evidence-based decision-making in Maternal, Newborn and Child Health and Nutrition (MNCH&N) at the municipality, district, and national levels in Mozambique. METHODS: We used a case study design to capture the experiences of decision-makers and analysts (n = 24) who participated in evidence-based decision-making processes related to health policies and interventions to improve MNCH&N in diverse decision-making contexts (district, municipality, and national levels) in 2014-2017, in Mozambique. We examined six case studies, at the national level, in Maputo City and in two districts of Sofala Province and two of Zambézia Province, using individual in-depth interviews with key informants and a document review, for three weeks, in July 2018. RESULTS: Our analysis highlighted various challenges for evidence-based decision-making for MNCH&N, at national, district, and municipality levels in Mozambique, including limited demand for evidence, limited capacity to use evidence, and lack of trust in the available evidence. By contrast, access to evidence, and availability of evidence were viewed positively and seen as potential facilitators. Organizational capacity for the demand and use of evidence appears to be the greatest challenge; while individual capacity is also a barrier. CONCLUSION: Evidence-based decision-making requires that actors have access to evidence and are empowered to act on that evidence. This, in turn, requires alignment between those who collect data, those who analyze and interpret data, and those who make and implement decisions. Investments in individual, organizational, and systems capacity to use evidence are needed to foster practices of evidence-based decision-making for improved maternal and child health in Mozambique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".