Community-Based Cluster Randomized Controlled Trial: Empowering Households to Identify and Provide Appropriate Care for Low-Birthweight Newborns in Nepal
Bibliographic record
Abstract
Abstract Background: Most newborn deaths occur among those of low birthweight (LBWt), due to prematurity &/or impaired fetal growth. Simple practices can mitigate this risk. In low-income country settings where many births occur at home, strategies are needed that empower mothers to determine if their babies are higher risk and take protective measures. Earlier studies suggest that foot-length may be a good proxy for birthweight. An earlier Nepal study found a 6.9cm cut-off performed relatively well, differentiating normal from low birthweight.Methods: Community-based, cluster-randomized controlled trial. Objective: to determine whether family-administered screening, with targeted messages improves care practices known to mitigate LBWt risks. Participants: pregnant women participating in a parent trial in rural Nepal. Women were given a 6.9cm card to assess whether the baby’s foot is small; if so, to call number on the card for advice. Follow-up visits were made over 2 weeks following the birth, assessing for: reported skin-to-skin thermal care, and care-seeking outside the home; restricting to low birthweight (using 2 cutoffs: 2,500g and 2,000g). Randomization: 17 clusters intervention, 17 control. The study also documented steps along the presumed causal chain from intervention through behavioral impact.Results: 2,022 into intervention, 2,432 into control. Intervention arm: 519 with birthweight <2,500g (vs. 663 among controls), of which 503 were available for analysis (vs. 649 among controls). No significant difference on care-seeking; for those <2,500g RR 1.13 (95%CI: 0.97-1.131). More of those in the intervention arm reported skin-to-skin thermal care than among controls; for those <2,500g RR 2.50 (95%CI: 2.01-3.1). Process measures suggest this apparent effect cannot be attributed to the intervention; the card performed poorly as a proxy for LBWt, misclassifying 84.5% of those <2,000 as normal.Conclusions: Although the trial found an apparent effect on one key behavioral outcome, this cannot be attributed to the intervention; most likely it was a result of pure chance. Other approaches are needed for identifying at-risk babies in such settings, and targeting them for appropriate care messaging.Trial registration : clinicaltrials.gov identifier: NCT02802332, registered 16 June 2016, https://clinicaltrials.gov/ct2/show/NCT02802332
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".