Effect of nutrition education on the nutritional status of pregnant women in Robe and Goba Towns, Southeast Ethiopia, using a cluster randomized controlled trial
Bibliographic record
Abstract
Maternal malnutrition is pervasive throughout the world, notably in sub-Saharan Africa (SSA), including Ethiopia. This study aimed to assess the effect of nutrition education on the nutritional status of pregnant women in urban settings in Southeast Ethiopia. A community-based two-arm parallel cluster randomized controlled trial was conducted among 447 randomly selected pregnant women attending antenatal care (224 intervention and 223 control). We used a multistage cluster sampling technique followed by systematic sampling to select the pregnant women. Pregnant women who participated in the intervention arm received six nutrition education sessions. Women in the control group received standard care. A nonstretchable mid-upper arm circumference (MUAC) tape was used to measure the MUAC. A linear mixed effects model (LMM) was used to evaluate the effect of the intervention on MUAC, accounting for the clustering. The net mean ± standard error of MUAC between the intervention and control groups was 0.59 ± 0.05 (P < 0.0001). The multivariable LMM indicated that having received nutrition education interventions (β = 0.85, 95% CI 0.60, 1.12, P < 0.0001) improved the MUAC measurement of pregnant women. Thus, nutrition education during pregnancy will combat undernutrition among pregnant women.Trial Registration: Clinicaltrials.gov (PACTR202201731802989), retrospectively registered on 24/01/2022.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".