The prevalence and predictors of household food insecurity among adolescents in Canada
Bibliographic record
Abstract
OBJECTIVES: Household food insecurity is almost four times more prevalent among adolescents than among older adults in Canada, and it adversely affects their health. Our objective was to describe the sociodemographic and geographic patterning of household food insecurity among adolescents. METHODS: Our analytic sample comprised all 12-17-year-old respondents to the 2017-2018 Canadian Community Health Survey with complete data on household food insecurity (n = 8416). We used bivariate and multivariable logistic regression models to identify respondent- and household-level sociodemographic characteristics associated with household food insecurity. RESULTS: The prevalence of household food insecurity among adolescents was 20.7%. The adjusted odds of food insecurity were significantly elevated among adolescents who identified as Black or Indigenous (aOR 1.80), those living with a single parent (aOR 1.60), those living with a greater number of children ≤ 5 years (aOR 1.45) or 12-17 years (aOR 1.25), those in rented accommodation (aOR 1.98), those in households with only secondary school education (aOR 1.38), and those in households reliant on social assistance (aOR 2.03). Higher before-tax income was protective (aOR 0.99). In comparison with Ontario, the adjusted odds of food insecurity among adolescents were higher in Nunavut (aOR 6.77), Northwest Territories (aOR 2.11), and Alberta (aOR 1.48), and lower in Manitoba (aOR 0.66). CONCLUSION: The markedly higher odds of exposure to household food insecurity among adolescents who are Black or Indigenous and those living in households characterized by markers of social and economic disadvantage highlight the need for more effective policy interventions to protect vulnerable families from this hardship.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".