Prevalence of Bipolar I and II Disorder in Canada
Bibliographic record
Abstract
OBJECTIVE: Current epidemiologic knowledge about bipolar disorder (BD) in Canada is inadequate. To date, only 3 prevalence studies have been conducted: only 1 was based on a national sample, and none distinguished between BD I and II. The objective of this study was to estimate the prevalence of BD I and II in Canada in 2012. METHOD: Data were obtained from the 2012 Canadian Community Health Survey: Mental Health and Well-being, a cross-sectional survey of a nationally representative sample of household residents ages 15 years and older (n = 25 113). The survey response rate was 68.9%. Interviews were based on the World Health Organization Composite International Diagnostic Interview (CIDI). Prevalence was estimated using generalized linear modelling. Prevalence of self-reported diagnosis of BD and use of lithium were also estimated. RESULTS: The estimated lifetime prevalence of BD I and II (based on the CIDI) in Canada in 2012 was 0.87% (95% CI 0.67% to 1.07%) and 0.57% (95% CI 0.44% to 0.71%), respectively. Prevalence did not differ by sex. The estimated prevalence of self-reported BD was 0.87% (95% CI 0.65% to 1.07%). There was a lack of congruence between CIDI-defined and self-reported BD, and few people taking lithium were positive for BD on the CIDI, which raises some concerns about the validity of the CIDI's assessment of BD. CONCLUSIONS: These prevalence estimates align with those reported in prior literature. However, caution should be exercised when interpreting general population studies that use CIDI-defined BD owing to the possibility of misclassification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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".