Differences in Prevalence and Treatment of Bipolar Disorder among Immigrants: Results from an Epidemiologic Survey
Bibliographic record
Abstract
OBJECTIVE: To add to the limited data on the prevalence, clinical characteristics, and treatment of bipolar disorder (BD) among immigrants. METHOD: Data were obtained from a large epidemiologic survey, the Canadian Community Health Survey-Mental Health and Well-Being (CCHS 1.2). Lifetime prevalence rates of BD were compared between immigrant and nonimmigrant respondents. Among BD subjects (n = 831), sociodemographic, clinical, and mental health treatment use variables were compared based on immigrant status. Logistic regression was used to determine the correlates of lifetime contact with a mental health professional and 12-month psychotropic medication use. RESULTS: Lifetime prevalence rate of CCHS 1.2-defined BD was significantly lower among immigrant, compared with nonimmigrant, participants (1.50% and 2.27%, P = 0.01). There were few sociodemographic or clinical differences, yet immigrants with BD were significantly less likely to report any lifetime contact with mental health professionals (OR = 0.25, 95% CI 0.13 to 0.50, P < 0.001). Past-year psychotropic medication use was numerically lower among immigrants with BD (24.5% and 41.0%); however, this did not reach statistical significance when controlling for other factors (OR = 0.49, 95% CI 0.24 to 1.01, P = 0.05). CONCLUSIONS: Based on the results of this study, there are in the range of 56 000 to 104 000 immigrants with BD in Canada. Further efforts are needed to better understand and address the barriers to mental health treatment use among immigrants who have BD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".