The association between food insecurity and academic achievement in Canadian school-aged children
Bibliographic record
Abstract
OBJECTIVE: Education is a crucial social determinant of health. Food insecurity can be detrimental to children's academic achievement, potentially perpetuating a cycle of poverty and food insecurity. We aimed to assess the relationship between food insecurity and academic achievement in Canadian school-aged children. DESIGN: Cross-sectional study of children and parents. Parents completed the short-form Household Food Security Survey Module and questions about income and education level (socio-economic status). Children completed FFQ. Data were prospectively linked to children's performance on standardized exams written one year later. Mixed-effect logistic regression was employed to assess the relationship between food insecurity and likelihood of meeting academic expectations adjusting for socio-economic status, diet quality and potential confounders. SETTING: Nova Scotia, Canada in 2011-2012. SUBJECTS: Students (n 4105) in grade 5 (10-11 years; 2167 girls) and their parents. RESULTS: Low food security was reported by 9·8 % of households; very low food security by 7·1 % of households. Students from low-income households and reporting poor diet quality were less likely to do well in school. Children who lived in households reporting very low food security had 0·65 times the odds (OR=0·65; 95 % CI 0·44, 0·96) of meeting expectations for reading and 0·62 times the odds (OR=0·62; 95 % CI 0·45, 0·86) of meeting expectations for mathematics. CONCLUSIONS: Very low household insecurity is associated with poor academic achievement among children in Nova Scotia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".