Changes in households’ vulnerability to food insecurity in Canada before and after the COVID-19 pandemic
Bibliographic record
Abstract
Background: The prevalence of household food insecurity in the 10 provinces rose from 16.8% in 2019 to 18.4% in 2022 and 22.9% in 2023. This study examines whether and how the sociodemographic and economic patterning of households' vulnerability to food insecurity changed across these years. Data and methods: Using data from the master files for households in the 10 provinces from the 2018, 2021, and 2022 cycles of the Canadian Income Survey, year-specific logistic regression models were conducted to estimate the predicted probability of household food insecurity by sociodemographic and economic characteristics. The predicted probability of food insecurity was also charted in relation to household income from the prior tax year, expressed in 2022 constant dollars and adjusted for household size, for each survey year. Results: The probability of food insecurity increased significantly for most households, irrespective of the sociodemographic or economic characteristics considered. In 2019 and 2022, households receiving 50% or more of their income from employment or self-employment had a lower probability of food insecurity than those with a smaller proportion of their income from employment, but there was no difference between these groups in 2023. The probability of food insecurity was significantly higher in 2022 than 2019 at all household income levels above $20,000 and higher along the entire household income continuum in 2023 than 2022. Interpretation: The probability of food insecurity is highest for low-income households, but food insecurity is becoming more prevalent among moderate- and higher-income households, and reliance on employment income is no longer protective against food insecurity.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".