Individual and community-level determinants of underweight among lactating mothers in Ethiopia: A multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Determining the nutritional status of lactating women is important because underweight lactating mothers will have low energy levels and reduced cognitive abilities, which will affect the inadequate care of their young children. Thus, malnutrition is passed down from generation to generation, perpetuating the vicious cycle. There is scarce national data on determinants of underweight among lactating mothers in Ethiopia. Hence, this study aimed to identify individual and community-level determinants of underweight among lactating mothers in Ethiopia. METHODS: Data from the Ethiopian Demographic and Health Survey (EDHS) from 2016 were used. A total of 3848 lactating mothers were included in this study, and a multilevel, multivariable logistic regression model was fitted to identify determinants of underweight among lactating mothers. RESULTS: The odds of being underweight among rural lactating mothers were 65% higher (AOR = 1.65, 95% CI = 1.13, 2.41) than lactating mothers in the urban area. The odds of being underweight among lactating mothers who have toilet facilities were 33% lower (AOR = 0.67, 95%CI = 0.54, 0.83) compared with those do not have toilet facilities. Those mothers in the age group of 25-34 years and greater than 35 years had (AOR = 0.61,95%CI = 0.48, 0.79), and (AOR = 0.66, 95%CI = 0.47, 0.95) times lower chance of being underweight compared with those who had 15-24 years of age, respectively. The likelihood of being underweight among lactating mothers in high community poverty (AOR = 1.40, 95%CI = 1.08, 1.82) was higher than the lower community poverty level. CONCLUSION: Underweight among lactating mothers was significantly associated with individual-level (age and toilet facilities) variables and community-level (residence and community poverty). Therefore, focusing on these identified factors could improve underweight among lactating mothers in Ethiopia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".