User fees removal and community-based management of undernutrition in Burkina Faso: what effects on children’s nutritional status?
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of an intervention combining user fees removal with community-based management of undernutrition on the nutrition status in children under 5 years of age in Burkina Faso. DESIGN: The study was a non-equivalent control group post-test-only design based on household survey data collected 4 years after the intervention onset in the intervention and comparison districts. Additionally, we used propensity score weighting to achieve balance on covariates between the two districts, followed by logistic multilevel modelling. SETTING: Two health districts in the Sahel region. PARTICIPANTS: Totally, 1116 children under 5 years of age residing in 41 intervention communities and 1305 from 51 control communities. RESULTS: When comparing children living in the intervention district to children living in a non-intervention district, we determined no differences in terms of stunting (OR = 1·13; 95 % CI 0·83, 1·54) and wasting (OR = 1·21; 95 % CI 0·90, 1·64), nor in severely wasted (OR = 1·27; 95 % CI 0·79, 2·04) and severely stunted (OR = 0·99; 95 % CI 0·76, 1·26). However, we determined that 3 % of the variance of wasting (95 % CI 1·25, 10·42) and 9·4 % of the variance of stunting (95 % CI 6·45, 13·38) were due to systematic differences between communities of residence. The presence of the intervention in the communities explained 2 % of the community-level variance of stunting and 3 % of the community-level variance of wasting. CONCLUSIONS: With the scaling-up of the national free health policy in Africa, we stress the need for rigorous evaluations and the means to measure expected changes in order to better inform health interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".