Common and Unique Risk Factors and Comorbidity for 12-Month Mood and Anxiety Disorders among Canadians
Bibliographic record
Abstract
OBJECTIVE: To explore the common and unique risk factors for mood and anxiety disorders. What sociodemographic, psychological, and physical risk factors are associated with mood and anxiety disorders and their comorbidities? What is the impact of multiple risk factors? METHOD: Data from the Canadian Community Health Survey: Mental Health and Well-Being were analyzed. Appropriate sampling weights and bootstrap variance estimation were employed. Multiple logistic regression was used to estimate odds ratios and confidence intervals. RESULTS: The annual prevalence of any mood disorder was 5.2%, and of any anxiety disorder 4.7%. Major depressive episode was the most prevalent mood and anxiety disorder (4.8%), followed by social phobia, panic disorder, mania, and agoraphobia. Among people with mood and anxiety disorders, 22.4% had 2 or more disorders. Risk factors common to mood and anxiety disorders were being young, having lower household income, being unmarried, experiencing greater stress, having poorer mental health, and having a medical condition. Unique risk factors were found: major depressive episode and social phobia were associated with being born in Canada; panic disorder was associated with being Caucasian; lower education was associated with panic and agoraphobia; and poor physical health was associated with mania and agoraphobia. People who were young, unmarried, not fully employed, and had a medical condition, greater stress, poorer self-rated mental health, and dissatisfaction with life, were more likely to have a comorbid mood and (or) anxiety disorder. As the number of common risk factors increases, the probability of having mood and anxiety disorders also increases. CONCLUSIONS: Common and unique risk factors exist for mood and anxiety disorders. Risk factors are additive in increasing the likelihood of disease.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".