Effectiveness of community health workers in improving early initiation and exclusive breastfeeding rates in a low‐resource setting: A cluster‐randomized longitudinal study
Bibliographic record
Abstract
Abstract Little evidence exists in Kenya on the potential of community health workers (CHWs) in promoting exclusive breastfeeding (EBF) and early breastfeeding initiation (EBI) in resource‐restricted settings where very low EBF rates (2% to 12%) have been documented. The study utilized CHWs and assessed their effectiveness in promoting EBF and EBI. The cluster‐randomized longitudinal design was used and sixteen villages from Kiandutu Slum in Thika randomly assigned into either intervention group (IG) or comparison group (CG). Pregnant women attending Maternal Child Health (MCH) clinic were recruited. The IG received nutrition education sessions conducted by CHWs at home, two prenatally and six postnatally, plus the routine MCH care. The CG went through routine MCH care only. Infants feeding data were collected at 6, 10, 14, and 24 weeks postpartum by research assistants blinded to the intervention allocation. Differences in EBF and EBI in the two groups were tested using χ2 tests, Kaplan–Meier survival analysis and generalized estimating equations. Of the 526 recruited in the study, 431 remained and were included in the analysis (IG = 176) and CG (225). The prevalence of EBF at 24 weeks was 45.3% in the IG compared with 15.0% in the CG, revealing a statistically significant difference log rank = 20.277, (1, n = 314) p < .001. The difference was not statistically significant in EBI prevalence between the IG (58.2%) and CG (60.3%; χ2 = 0.008, p = .928). The CHWs have potential effectiveness in promoting EBF but not EBI. The link between the health center and CHWs should be strengthened to promote EBF.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".