Food insecurity is associated with mental health problems among Canadian youth
Bibliographic record
Abstract
BACKGROUND: Research has linked food insecurity to mental health problems, though little is known about this relationship among Canadian youth. We investigate the association between food insecurity severity and mental illnesses in a nationally representative youth sample. METHODS: We sampled 55 700 youth 12-24 years from recent cycles of Canadian Community Health Survey. Household food insecurity was measured using a standard 18-item questionnaire. We fitted Poisson regressions on self-rated mental health and diagnosed mood and anxiety disorders, controlling for sociodemographic confounders. Clinical assessments of emotional distress, major depression and suicidal ideation were examined in subsamples with available data. We stratified the sample by gender, age and survey cycle to test potential demographic heterogeneity. RESULTS: One in seven youth lived in marginal (5.30%), moderate (8.08%) or severe (1.44%) food insecurity. Results showed that food insecurity was associated with higher likelihood of every mental health problem examined. The association was graded, with more severe food insecurity linked to progressively worse mental health. Notably, marginal, moderate and severe food insecurity were associated with 1.77, 2.44 and 6.49 times higher risk of suicidal thoughts, respectively. The corresponding relative risk for mood disorders were 1.57, 2.00 and 2.89; those for anxiety disorders were 1.41, 1.65 and 2.58. Moderate food insecurity was more closely associated with mental health problems in 18-24 year olds than in 12-17 year olds. CONCLUSIONS: Food insecurity severity was associated with poorer mental health among Canadian youth independent of household income and other socioeconomic differences. Targeted policy intervention alleviating food insecurity may improve youth mental health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".