Mobility impairments and geographic variation in vulnerability to household food insecurity
Bibliographic record
Abstract
Studies indicate an association between disability and higher rates of household food insecurity (HFI). Geographic variation in this relationship has not been explored despite the potential influence of economic and political contexts, including costs of living and disability social assistance. This study examines the association between mobility impairment and HFI within and across Canada considering the possible role of population composition, contextual, and collective influences. Using data from 217,094 adults from the 2007/08, 2009/10, 2013/14, and 2015/16 Canadian Community Health Survey, multivariate logistic regression models examined associations between mobility impairment and HFI controlling for socio-demographic factors and geography of residence (i.e., province, region, and urban/rural status). Subsequent analysis of 14,353 surveyed adults with mobility impairments was conducted to examine geographic and socio-demographic factors associated with HFI in this population. Adults with mobility impairments had elevated odds of HFI of 3.85 (95% CI: 3.49-4.24), when adjusting for age, sex, and geography of residence and 2.11 (95% CI: 1.89-2.35) adjusting for additional socio-demographic characteristics. Across Canada, mobility impaired adults experienced greater odds of HFI. Significantly lower odds of HFI were found for mobility impaired adults living in Newfoundland, Alberta, and Saskatchewan compared to Ontario when adjusting for age and sex, and in Quebec when controlling for additional socio-demographic factors. Socioeconomic factors and age accounted for most variation in HFI in this population, suggesting the importance of poverty reduction strategies that reduce vulnerability to HFI across the 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".