Household Food Insecurity and Psychosocial Dysfunction in Ecuadorian Elementary Schoolchildren
Bibliographic record
Abstract
Household food insecurity (HFI) is a major global public health and pediatric concern due to its reported association with adverse child nutrition, growth, and health outcomes. Psychosocial dysfunction is a major cause of childhood disability. US and Canadian studies have linked HFI to poorer overall psychosocial dysfunction and specific dysfunction types in school-aged children, i.e., internalizing, externalizing, and attention behaviors. However, it is uncertain whether prior findings are generalizable to low- and middle-income country (LMIC) settings. We conducted a cross-sectional study to explore the association of HFI with psychosocial dysfunction in 6-12-year-old public elementary schoolchildren (n=279) residing in low-income neighborhoods in Quito, Ecuador. Maternal caregivers were interviewed to obtain data on child psychosocial dysfunction (Pediatric Symptom Checklist, PSC), food security (Household Food Security Survey Module), and maternal mental health (SF-36 Mental Composite Summary). Capillary blood samples were obtained from child participants to measure hemoglobin levels. The data were analyzed using general linear models with adjustment for covariates. The results revealed that HFI was associated with significantly higher overall average PSC scores (p=0.002) and with internalizing (p=0.001) and externalizing (p=0.03) but not attention subscale scores. However, anemia was independently associated with PSC attention subscale scores (p=0.015). This is the first study to report on the relationship between HFI and psychosocial dysfunction in school-aged children in a LMIC setting. It highlights the importance of improving policies and programs protecting vulnerable households from HFI. In addition to improving health and nutrition, such improvements could potentially reduce the burden of child psychosocial dysfunction.
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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 | 0.000 | 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) | 0.001 | 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".