Female sex and food insecurity in relation to self-reported poor or fair mental health in Canadian adults: a cross-sectional study using national survey data
Bibliographic record
Abstract
BACKGROUND: Women with food insecurity are at higher risk for mental health disorders. This study examined the joint effect of female sex and food insecurity on self-reported poor or fair mental health in Canadian adults. METHODS: The analysis was based on data from adults (age ≥ 18 yr) who participated in the Canadian Community Health Survey (CCHS) 2015-2016. We determined past-year food security level (secure, moderately insecure or severely insecure) based on 18 questions. We used log-binomial regression to explore associations of sex and food insecurity with self-reported poor or fair mental health. We measured additive interaction between female sex and food insecurity using relative excess risk due to interaction (RERI). RESULTS: The overall response rate for the CCHS was 59.5%. Data for 61 446 respondents were analyzed. Poor or fair mental health was reported by 4107 participants (6.1% when weighted to the Canadian population). Increased risk of poor or fair mental health was associated with female sex (prevalence ratio [PR] 1.22, 95% confidence interval [CI] 1.12 to 1.31), and moderate (PR 2.50, 95% CI 2.21 to 2.82) and severe (PR 4.03, 95% CI 3.59 to 4.52) food insecurity. Significant additive interaction between female sex and severe food insecurity was found for those aged 40-64 years (RERI 1.38, 95% CI 0.29 to 2.47), and the PR for poor or fair mental health for severely food-insecure women was 5.55 (95% CI 4.48 to 6.89) compared to food-secure men of the same age group. INTERPRETATION: Poor or fair mental health is common in the food-insecure population, and there exists synergism between female sex and severe food insecurity among middle-aged people. This suggests the need to develop targeted mental health support strategies for food-insecure people, specifically middle-aged women.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".