Living with Migraine in Canada – A National Community-Based Study
Bibliographic record
Abstract
OBJECTIVE: To develop a detailed profile of individuals living with migraine in Canada. Such a profile is important for planning and administration of services. METHODS: The 2011-2012 Survey of Living with Neurological Conditions in Canada (SLNCC), a cross-sectional community-based survey, was used to examine a representative sample of migraineurs (N = 949) aged 15 years and older. Several health-related variables were examined (e.g., general health, health utility index (HUI) [a measure of health status and health-related quality of life, where dead = 0.00 and perfect health = 1.00], stigma, depression, and social support). Respondents were further stratified by sex, age, and age of migraine onset. Weighted overall and stratified prevalence estimates and odds ratios, both with 95% CIs, were used to estimate associations. RESULTS: Overall, males had poorer health status compared with females (e.g., mean HUI was 0.67 in males vs. 0.82 in females; men had over two times the odds of their migraine limiting educational and job opportunities compared with females). Poorer health-related variables were seen in the older age groups (35-64 years/≥65 years) compared with the 15-34-year age group. There were no differences between those whose migraine symptoms began before versus after the age of 20 years. CONCLUSIONS: In this Canadian sample, migraine was associated with worse health-related variables in men compared with women. However, both men and women were significantly affected by migraine across various health-related variables. Thus, it is important to improve clinical and public health interventions addressing the impact of migraine across individuals of all ages, sexes, and sociodemographic backgrounds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".