Descriptive Epidemiology of Generalized Anxiety Disorder in Canada
Bibliographic record
Abstract
OBJECTIVE: The first national survey to assess the prevalence of generalized anxiety disorder (GAD) in Canada was the 2012 Canadian Community Health Survey: Mental Health and Well-Being (CCHS-MH). The World Mental Health Composite International Diagnostic Interview (WMH-CIDI), used within the representative sample of the CCHS-MH, provides the best available description of the epidemiology of this condition in Canada. This study uses the CCHS-MH data to describe the epidemiology of GAD. METHOD: The analysis estimated proportions and odds ratios and used logistic regression modelling. All results entailed appropriate sampling weights and bootstrap variance estimation procedures. RESULTS: The lifetime prevalence of GAD is 8.7% (95% CI, 8.2% to 9.3%), and the 12-month prevalence is 2.6% (95% CI, 2.3% to 2.8%). GAD is significantly associated with being female (OR 1.6; 95% CI, 1.3 to 2.1); being middle-aged (age 35-54 years) (OR 1.6; 95% CI, 1.0 to 2.7); being single, widowed, or divorced (OR 1.9; 95% CI, 1.4 to 2.6); being unemployed (OR 1.9; 95% CI, 1.5 to 2.5); having a low household income (<$30 000) (OR 3.2; 95% CI, 2.3 to 4.5); and being born in Canada (OR 2.0; 95% CI, 1.4 to 2.8). CONCLUSIONS: The prevalence of GAD was slightly higher than international estimates, with similar associated demographic variables. As expected, GAD was highly comorbid with other psychiatric conditions but also with indicators of pain, stress, stigma, and health care utilization. Independent of comorbid conditions, GAD showed a significant degree of impact on both the individual and society. Our results show that GAD is a common mental disorder within Canada, and it deserves significant attention in health care planning and programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".