‘Hunger in early life’: exploring the prevalence and correlates of child food insecurity in Canada
Bibliographic record
Abstract
Abstract Background Although food is a basic human right, food insecurity remains a major problem in the Global North including Canada. Children constitute a subgroup that is particularly vulnerable to food insecurity, with recent evidence showing that 1 in 6 Canadian children are food insecure. The rising rate of child food insecurity alongside its links with several adverse health outcomes reinforce the need to pay attention to its determinants. Although food insecurity is a multidimensional phenomenon shaped by diverse factors, in the Global North, including Canada, it is generally framed as a financial problem. Consequently, food policy has largely prioritized income support programs to the neglect of potentially important non-monetary factors. These non-monetary factors are also rarely explored in the literature despite their potentially relevant role in shaping policy responses to child food insecurity. Drawing data from the Canadian Community Health Survey (N = 21,455 households with children) and broadening the scope of potential predictors, this paper examined the correlates of child food insecurity in Canada. Results Findings show children in visible minority households (OR = 1.12, p < 0.01), single-parent households (OR = 1.55, p < 0.001), households with five or more members (OR = 1.35, p < 0.001), households with the highest level of education being secondary education or lower (OR = 1.14, p < 0.05), households where the adult respondent reported a very weak sense of community belonging (OR = 1.32, p < 0.001), poor physical health (OR = 1.61, p < 0.001) and poor mental health (OR = 1.61, p < 0.001) had higher odds of being food insecure. Children in lower income households were also more likely to be food insecure. Conclusions This study demonstrates the multidimensional nature of child food insecurity and highlights the need for food policy to pay attention to relevant social factors. Although commonly highlighted economic factors such as household income and employment status remain important correlates of child food insecurity in Canada, non-monetary factors such as visible minority status, sense of community belonging and living arrangement of parents/guardians are noteworthy predictors of child food insecurity that need equal policy attention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".