Flourishing Despite Migraines: A Nationally Representative Portrait of Resilience and Mental Health among Canadians
Bibliographic record
Abstract
Objective 1) To examine the relationship between migraine status and complete mental health (CMH) among a nationally representative sample of Canadians; 2) To identify significant correlates of CMH among those with migraine. Methods Secondary analysis of the nationally representative Canadian Community Health Survey – Mental Health (CCHS-MH) (N=21,108). Bivariate analyses and a series of logistic regression models were performed to identify the association between migraine status and CMH. Significant correlates of CMH were identified in the sample of those with migraine (N=2,186). Results Individuals without a history of migraine had 72% higher odds of being in CMH (OR=1.72; 95% CI=1.57, 1.89) when compared with those with a history of migraine. After accounting for physical health and mental health problems, the relationship between migraine status and CMH was reduced to non-significance, with both groups having an approximately equal likelihood of achieving CMH (OR=1.03; 05% CI=(0.92, 1.15). Among those with migraine, factors that were strongly associated with CMH were a lack of a history of depression, having a confidant, and having an income of $80,000 or more. Conclusion Clinicians and health care providers should also address co-occurring physical and mental health issues to support the overall well-being of migraineurs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".