A mixed-methods study of the drivers of stunting reduction among children under-5 in Sierra Leone, 2005–2017
Bibliographic record
Abstract
BACKGROUND: Childhood stunting prevalence declined dramatically in Sierra Leone after the end of the civil war in 2002, despite major challenges of postwar recovery and the West Africa Ebola epidemic in 2014-2016. OBJECTIVES: This study aims to identify the factors associated with declining stunting prevalence among children under-5 in Sierra Leone between 2005 and 2017. METHODS: Mixed methods including literature review, primary qualitative research, narrative policy and program review, and secondary analysis of household survey data on children under-5 with valid height-for-age z-scores (HAZ) (n = 4915 in 2005, n = 7736 in 2010, and n = 11,447 in 2017), including Oaxaca-Blinder decomposition analysis. RESULTS: Under-5 stunting prevalence declined from 46.9% in 2005 to 26.4% in 2017 and mean HAZ increased from -1.75 to -1.13, with some narrowing of inequalities in child growth by wealth, maternal education, and sex, but not between urban and rural children. Stakeholders highlighted several policies and programs that evidenced government commitment to improving maternal and child health and nutrition and likely contributed to improved child growth over time, including the Free Health Care Initiative introduced in 2010. Our quantitative modeling predicted only 29% of the observed 0.62 HAZ increase over the study period but highlighted expanded coverage of antenatal care, increased urbanization, and increased household wealth as key drivers of nutritional change over time. CONCLUSIONS: Rapid reduction in under-5 stunting in Sierra Leone coincided with strong economic growth and declining urban poverty, along with high-profile health and development policies supported by external partners that targeted and/or benefited poorer and rural populations especially. Had the Ebola and COVID-19 epidemics not occurred, positive pre-2014 trajectories in child growth and other maternal and child health and nutrition outcomes in Sierra Leone may well have continued, yielding gains even larger than those observed over the study period.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".