Shelter Expenditures Increase Vulnerability to Household Food Insecurity
Bibliographic record
Abstract
Objective Income is a strong predictor of food insecurity, but the income necessary for food security is a function of household expenditures. The objective of this study is to characterize the relationship between income, shelter expenditures and food insecurity in Canada. Methods Data from Canada's 2010 Survey of Household Spending were converted into budget shares (expenditure‐to‐income ratio) for food, utilities and housing (rent or mortgage). Logistic regression was used to assess the relationship between budget shares and food insecurity, defined as any affirmative response to the 10‐item adult food security scale. Among food insecure renters, linear regression was used to test the association between budget shares and severity of food insecurity, defined by a raw score of 1 to 10. All models were adjusted for sociodemographic factors. Results Renters were 3 times more likely to be food insecure than homeowners. Having a mortgage doubled the odds of food insecurity for homeowners. For every 1% increase in the proportion of income allocated to utilities, the odds of food insecurity increased by 5.2% for renters and 6.6% for homeowners. The odds of food insecurity also rose with the budget share for rent among renters and with the budget shares for mortgage and food among homeowners, but these effects were much weaker. Among food insecure renters, a 1% increase in the proportion of income allocated to utilities and rent increased the food insecurity raw score by 3.9 and 1.2 points, respectively; food budget share did not affect severity of food insecurity. Conclusion Small increments in shelter expenditures, particularly utilities expenses, appear to contribute substantially to the risk of household food insecurity and to severity of food insecurity. Funded by Canadian Institutes of Health Research (115208).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".