Household food insecurity among persons with disabilities in Canada: Findings from the 2021 Canadian Income Survey
Bibliographic record
Abstract
Background: Income-related food insecurity is an important determinant of health. Persons with disabilities are at a higher risk of experiencing household food insecurity (HFI) than those without disabilities. The main objectives of this study were to estimate the prevalence of HFI for persons with different types, numbers, and severity of disabilities, and to examine sociodemographic correlates of HFI among this group. Data and methods: Data from the 2021 Canadian Income Survey (CIS) were used. Disability status was assessed using the short version of the Disability Screening Questions module for one randomly selected household respondent. The Household Food Security Survey Module measured HFI as marginal, moderate, or severe. Weighted descriptive and multivariable analyses were conducted to estimate the prevalence of HFI and analyze the association between various socioeconomic factors and HFI among the study sample. Results: Among CIS participants with disabilities (30% of the total CIS sample: 31 million persons), 26% reported some level of HFI, including 8% with severe HFI. The prevalence of HFI was 13% among those without disabilities. The prevalence of HFI was highest among those with learning, memory, cognition, and seeing disabilities (each at 36%). Levels of HFI were higher for those with more severe disabilities and with a greater number of disabilities. For persons with disabilities, the odds of HFI were two times higher, compared with persons without disabilities (adjusted odds ratio [AOR]: 2.5 [95% confidence interval (CI): 2.2, 2.7]), after adjustment for a range of sociodemographic covariates. Persons with disabilities who were in the lowest income quintile (AOR: 4.0 [95% CI: 3.2, 4.9]) and aged 45 to 54 (AOR: 2.9 [95% CI: 2.1, 4.1]) had the highest odds of HFI, compared with other persons with disabilities living in wealthier households and those aged 65 and older, respectively. Other risk factors included being in a one-parent household, living in the Prairies, and living in a dwelling not owned by the household. Interpretation: HFI prevalence among CIS participants with disabilities was higher than for persons without disabilities, even after adjustment for well-documented sociodemographic risk factors. Consistent monitoring of HFI among persons with disabilities can help inform any ongoing or newly developed poverty reduction strategies for this population.
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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 | 0.000 |
| 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".