Food Insecurity among Homeless Adults with Mental Illness
Bibliographic record
Abstract
BACKGROUND: The prevalence of food insecurity and food insufficiency is high among homeless people. We investigated the prevalence and correlates of food insecurity among a cohort of homeless adults with mental illness in Vancouver, British Columbia, Canada. METHODS: Data collected from baseline questionnaires in the Vancouver At Home study were analysed to calculate the prevalence of food insecurity within the sample (n = 421). A modified version of the U.S. Department of Agriculture's Adult Food Security Survey Module was used to ascertain food insecurity. Univariable and multivariable logistic regression were used to examine potential correlates of food insecurity. RESULTS: The prevalence of food insecurity was 64%. In the multivariable model, food insecurity was significantly associated with age (adjusted odds ratio [aOR] = 0.97; 95% CI: 0.95-0.99), less than high school completion (aOR = 0.57; 95% CI: 0.35-0.93), needing health care but not receiving it (aOR = 1.65; 95% CI: 1.00-2.72), subjective mental health (aOR = 0.97; 95% CI: 0.96-0.99), having spent over $500 for drugs and alcohol in the past month (aOR = 2.25; 95% CI: 1.16-4.36), HIV/AIDS (aOR = 4.20; 95% CI: 1.36-12.96), heart disease (aOR = 0.39; 95% CI: 0.16-0.97) and having gone to a drop-in centre, community meal centre or program/food bank (aOR = 1.65; 95% CI: 1.01-2.68). CONCLUSIONS: The prevalence of food insecurity was extremely high in a cohort with longstanding homelessness and serious mental illness. Younger age, needing health care but not receiving it, poorer subjective mental health, having spent over $500 for drugs and alcohol in the past month, HIV/AIDS and having gone to a drop-in centre, community meal centre or program/food bank each increased odds of food insecurity, while less than high school completion and heart disease each decreased odds of food insecurity. Interventions to reduce food insecurity in this population are urgently needed.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".