Child gender-related vulnerability and chronic malnutrition in Burkina Faso: the moderating role of the maternal education.
Bibliographic record
Abstract
Abstract Background: Chronic malnutrition in children is a growth disorder resulting from a prolonged lack of nutrient intake. It is a widespread health issue in West and Central Africa where it affects one third (1/3) of children under five (05). This paper highlights the mitigation of child’s gender-based chronic malnutrition risk through maternal education. Methods: This paper uses data from the 2010 Demographic and Health Survey in Burkina Faso. Univariate and bivariate descriptive analysis techniques, multilevel logistic regression and interaction analyses were used. Analyses were conducted separately for children aged 0-5 months, 6-23 months and 24-59 months. Results: The results showed that boys are at greater risk of suffering from chronic malnutrition than girls, even after considering control factors. This gender-based effect was observed in children in the 6-23 month and 24–59-month age subgroups. Furthermore, the interaction analysis showed that this gender effect is more pronounced in and even specific to children of uneducated mothers because of the latter’s inappropriate hygiene and sanitation practices. Conclusion: These results suggest that intensifying policies to encourage girls’ schooling and strengthening hygiene and sanitation measures among children would reduce or even eliminate gender-based inequalities in chronic malnutrition among children in Burkina Faso.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".