Developing data use capacity in the maternal, newborn, child health and nutrition sector in Malawi, Mali, Mozambique and Tanzania: an evolving strategy
Bibliographic record
Abstract
A longside the Sustainable Development Goals is a call for a "data revolution" that will "monitor progress, hold governments accountable, and foster sustainable development" [1].With recent technological advances, the amount and types of data available to governments have increased rapidly.However, there are gaps in data literacy needed to access, analyze and apply these data to policy and program decision making.In 2014, the United Nation' s Independent Expert Advisory Group on a Data Revolution for Sustainable Development called for data-focused capacity building in low-income countries [1].The National Evaluation Platform (NEP) aims to improve health and nutrition outcomes in women and children by strengthening the capabilities of government institutions to use data to guide Maternal, Newborn, and Child Health and Nutrition (MNCH&N) policies and programs.From 2014-2018, multi-institutional teams in Malawi, Mali, Mozambique and Tanzania -countries that are diverse geographically, linguistically and epidemiologically to increase the generalizability of the tools and lessons generated by the project -each built their own NEP.Participating institutions in each country included those that support MNCH&N through data collection, financing, policy development and/or program implementation.NEP engaged higher-level MNCH&N decision makers as members of NEP High-level Advisory or Steering Committees (HLAC) and technically-focused mid-level staff as members of NEP Technical Working Groups (TWG).A team of faculty from the Institute for International Programs at Johns Hopkins Bloomberg School of Public Health (IIP-JHU) provided tools, training and mentorship to these teams as part of a capacity building strategy that aimed to keep the NEP "country-led and country-owned".The overall NEP structure is described in more detail in Heidkamp' s 2017 publication [2].Here we describe the start and evolution of the NEP capacity building strategy and share key learnings from internal and external assessments across the four countries.Our experience can inform efforts by governments and development partners to catalyze a "data revolution" through investments in public sector capacity building.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".