The presence of the double burden of malnutrition in children and their mothers in Grand-Popo, Benin
Bibliographic record
Abstract
Background: Malnutrition is a public health problem, with 45% of child deaths attributed to undernutrition. Aim: The aim of this study was to investigate the presence of the double burden of malnutrition in children-mother pairs in Benin. Methods: An analytical cross-sectional approach was used to collect data from 408 randomly selected children aged 0–59 months and their mothers. A pretested questionnaire was used to collect information on demographic and socioeconomic status, and anthropometric measurements were used to estimate the nutritional status of children and body mass indexes of the mothers. The World Health Organization Anthro software was used to estimate Z-scores whereas SPSS version 25 was used for statistical analysis. Descriptive and regression analysis were carried out on the data. Results: The mean age of children was 24.45 ± 14.9 months and 53.2% were girls. The mean age of mothers was 27.99 ± 6.99, 93.1% were married and approximately 91.7% of households had a monthly income of US$60. Prevalence of wasting in children was 9.8% and stunting was 29.7%. The mean waist circumference of mothers was 79.95 ± 10.35 cm and 19.3% were classified as high metabolic risk. Body mass index measurements showed that 16.9% of mothers were overweight and 7.4% obese. Conclusions: Stunting and wasting were highly prevalent. There is a coexistence of underweight and overweight in Grand-Popo, Benin. The results drew attention to the need for coordination of preventive interventions to focus on the household food distribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".