The Relation between Food Insecurity and Mental Health Care Service Utilization in Ontario
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between household food insecurity status over a 12-month period and adults' use of publicly funded health care services in Ontario for mental health reasons during this period. METHODS: Data for 80,942 Ontario residents, 18 to 64 years old, who participated in the Canadian Community Health Survey in 2005, 2007-2008, 2009-2010, or 2011-2012 were linked to administrative health care data to determine individuals' hospitalizations, emergency department visits, and visits to psychiatrists and primary care physicians for mental health reasons. Household food insecurity over the past 12 months was assessed using the Household Food Security Survey Module. Logistic regression models were used to estimate the odds of mental health service utilization in the past 12 months by household food insecurity status, adjusting for sociodemographic factors and prior use of mental health services. RESULTS: In our fully adjusted models, in comparison to food-secure individuals, the odds of any mental health care service utilization over the past 12 months were 1.15 (95% confidence interval [CI], 1.04 to 1.29) for marginally food-insecure individuals, 1.39 (95% CI, 1.19 to 1.42) for moderately food-insecure individuals, and 1.50 (95% CI, 1.35 to 1.68) for severely food-insecure individuals. A similar pattern persisted across individual types of services, with odds of utilization highest with severe food insecurity. CONCLUSIONS: Household food insecurity status is a robust predictor of mental health service utilization among working-age adults in Ontario. Policy interventions are required to address the underlying causes of food insecurity and the particular vulnerability of individuals with mental illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 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".