Household food insecurity during the pre‐harvest period is associated with respiratory infections, but not stunting among 6–11 month old infants in rural Ghana
Bibliographic record
Abstract
Food insecurity is prevalent in rural Ghana, particularly during the months prior to harvest. We examined the association between pre‐harvest food insecurity and infants’ health and nutritional status. The cross‐sectional survey of 333 mothers and their infants aged 6–11 mo living in the Upper Manya Krobo district, included reported household food insecurity (HHFI), anthropometric measurements of infants and mothers, and mothers’ recall of symptoms of infants’ illnesses during the previous seven days. Multiple logistic regressions were conducted to examine how HHFI was associated with stunting and morbidity. Over one‐fifth of households experienced food insecurity in the previous month. Compared to infants in food secure homes, infants living in food insecure homes were twice as likely to experience cough (aOR = 2.26, 95% CI: 1.24 to 4.13), and tended to experience a runny nose (aOR = 1.82, 95% CI: 0.98 to 3.39). HHFI was not associated with diarrhea, fever or stunting. Efforts to improve infant's health status may need to include strategies that improve household food security, particularly during the pre‐harvest period. Funded by IDRC Doctoral Research Award 105938–99906075‐038 and McGill University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".