Food insecurity, childhood illness and maternal emotional distress in Ethiopia
Bibliographic record
Abstract
OBJECTIVE: The relationship between food insecurity, maternal emotional distress and childhood morbidity in resource-poor settings is not well clarified. The present study aimed to assess independent associations between household food insecurity and childhood morbidity and potential modifications by maternal emotional distress. DESIGN: A cross-sectional survey. A food security scale was used to assess household food insecurity; maternal reports were used to assess recent childhood illness; and the Hopkins Symptom Checklist was used to assess symptoms of emotional distress among mothers. SETTING: The Oromia Region, Ethiopia (rural area). SUBJECTS: A total of 936 mother-child pairs. RESULTS: Of 936 children assessed, 22·4% had experienced diarrhoea, 20·7% had cough and 21·5% had fever in the 2 weeks preceding the interview. Household food insecurity was reported by 39% of mothers. Greater food insecurity and greater maternal emotional distress were each independently associated with higher prevalence of cough and fever. Among mothers with low emotional distress, food insecurity was associated with a 2·3 times greater odds of diarrhoea in their children. CONCLUSIONS: Household food insecurity may increase the risk of childhood illness in rural Ethiopia, and children having mothers with greater emotional distress may be at highest risk. These findings highlight the importance of strengthening policy initiatives aimed at reducing the high prevalence of food insecurity and emotional distress 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".