Productivity Loss Associated with Disability from Migraine: A Canada-Wide Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Migraine can affect adults during their most productive years, yet few studies in Canada have examined the relationship between migraine-related disability and productivity loss. In particular, the impact of migraine on unpaid productivity loss has not been quantified. METHODS: In this cross-sectional study, employed adults living with migraine were recruited from across Canada to complete a web-based questionnaire. Migraine-related disability was assessed using the Migraine Disability Assessment questionnaire, and productivity loss was evaluated using the Valuation of Lost Productivity questionnaire. Multiple regression models were used to quantify the association between migraine-related disability level and productivity loss after adjusting for relevant clinical, occupational and sociodemographic covariates. RESULTS: There were 441 participants, of which 60.1% were female, and the mean (SD) age was 37.7 (10.9). Compared to participants with little to no migraine-related disability, hours of total productivity loss were higher among those with moderate disability (54.1 [95% CI: 10.2-98.1] adjusted hours per 3 months) and severe disability (110.5 [95% CI: 65.5-155.6] adjusted hours per 3 months); paid productivity loss was higher among participants with moderate disability (32.4 [95% CI: 3.1-61.8] adjusted hours per 3 months) and severe disability (61.6 [95% CI: 31.5-91.7] adjusted hours per 3 months); and unpaid productivity loss was greater in those with severe disability (43.5 [95% CI: 12.7-74.3] adjusted hours per 3 months). CONCLUSIONS: Greater migraine-related disability was associated with more total, paid and unpaid productivity loss among employed adults. These data will be valuable when evaluating the cost-effectiveness of emerging migraine therapies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".