Bibliographic record
Abstract
Objective: The original aim of this thesis was to estimate the prevalence of bipolar disorder (BD) including bipolar I (BDI) and bipolar II (BDII) disorder subtypes, in Canada in 2012 and to characterize persons with BD in terms of demographic features, health status, and mental health service utilization. During the course of this thesis, a third objective emerged which was to examine some indicators of validity of the World Mental Health version of the World Health Organization Composite International Diagnostic Interview (WMH-CIDI) classifications of BD. Methods: Data were from the Canadian Community Health Survey-Mental Health (CCHS-MH; n=25,113) Interviews were based on the WMH-CIDI. The WMH-CIDI uses algorithms to determine the presence or absence of BD. Alternative classification methods were also considered. Using proportions and generalized linear modeling, I estimated prevalence of BD, examined agreement among different methods for classifying BD and described the epidemiology of BD according to the different classifications in terms of demographic features, health status and impact, and mental health service utilization. Results: According the WMH-CIDI algorithm, the prevalence of BDI and BDII 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. I observed a lack of congruence between WMH-CIDI defined and self-reported BD, and few people taking lithium were positive for BD on the WMH-CIDI, which raises concern about the validity of the WMH-CIDI’s assessment of BD. Conclusion: Prevalence estimates using the WMH-CIDI align with those reported in prior literature. However, existing algorithms used to diagnose BD in the WMH-CIDI appear to result in a large proportion of misclassification. Fully structured interviews may be inaccurate for assessing BD. Future research should aim to develop and evaluate new methods of identifying BD in the general population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".