Bibliographic record
Abstract
AIM: The aim of this study was to examine the prevalence of household food insecurity in individuals reporting migraine within a large population-based sample of Canadians. METHODS: The Canadian Community Health Survey (CCHS) uses a stratified cluster sample design to obtain information on Canadians ≥12 years of age. Data on household food insecurity were assessed for individuals who reported having migraine or not, providing a current point prevalence. This was assessed for stability in two CCHS datasets from four and eight years earlier. Factors associated with food insecurity among those reporting migraine were examined and a logistic regression model of food insecurity was developed. We also examined whether food insecurity was associated with other reported chronic health conditions. RESULTS: Of 48,645 eligible survey respondents, 4614 reported having migraine (weighted point prevalence 10.2%). Food insecurity was reported by 14.8% who reported migraine compared with 6.8% of those not reporting migraine, giving an odds ratio of 2.4 (95% confidence interval 2.0-2.8%). This risk estimate was stable over the previous eight years. The higher risk for food insecurity was not unique to migraine and was seen with some, but not all, chronic health conditions reported in the CCHS. CONCLUSIONS: Food insecurity is more frequent among individuals reporting migraine in Canada.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".