Participation in a Community-Based Women's Health Education Program and At-Risk Child Development in Rural Kenya: Developmental Screening Questionnaire Results Analysis
Bibliographic record
Abstract
ABSTRACT Background: Over 43% of children living in low- and middle-income countries are at risk for developmental delays; however, access to protective interventions in these settings is limited. We evaluated the effect of maternal participation in Chamas for Change (Chamas)—a community-based women9s health education program during pregnancy and postpartum—and risk of developmental delay among their children in rural Kenya. Methods: We analyzed developmental screening questionnaire (DSQ) data from a cluster randomized controlled trial in Trans Nzoia County, Kenya (ClinicalTrials.gov, NCT03187873). Intervention clusters (Chamas) participated in community health volunteer-led, group-based health lessons twice a month during pregnancy and postpartum; controls had monthly home visits (standard of care). We screened all children born during the trial who were alive at 1-year follow-up. We labeled children with any positive item on the DSQ as “at-risk development.” We analyzed data using descriptive statistics and multilevel regression models (α=.05); analyses were intention-to-treat using individual-level data. Results: Between November 2017 and March 2018, we enrolled 1,920 pregnant women to participate in the parent trial. At 1-year follow-up, we screened 1,273 (689 intervention, 584 control) children born during the trial with the DSQ. Intervention mothers had lower education levels and higher poverty likelihood scores than controls (P<.001 and P=.007, respectively). The overall rate of at-risk development was 3.5%. Children in Chamas clusters demonstrated significantly lower rates of at-risk development than controls (2.5% vs. 4.8%, P=.025). Adjusted analyses revealed lower odds for at-risk development in the intervention arm (OR=0.50; 95% confidence interval=0.27, 0.94). Conclusions: Maternal participation in a community-based women9s health education program was associated with lower rates of at-risk development compared to the standard of care. Overall, rates of at-risk development were lower than expected for this population, warranting further investigation. Chamas may help protect children from developmental delay in rural Kenya and other resource-limited settings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".