Mood and Anxiety Disorders, Measurement, and Migrant Groups in Ontario
Bibliographic record
Abstract
Our objectives were to: (1) evaluate the current literature on the epidemiology of mood or anxiety disorders among migrant groups; (2) assess how current tools for measuring mood or anxiety disorders at the population level influence our understanding of the epidemiology by a) analyzing the concordance between two commonly used population measures, and b) using a Bayesian analysis to create a combined estimate using both measures; (3) estimate the prevalence and effects of potential risk factors on the prevalence of mood or anxiety disorders among first-generation migrant groups compared to the general population in Ontario. We conducted a systematic review and multiple secondary data analyses using data available from ICES to complete our objectives. Data sources included the 2012 Canadian Community Health Survey–Mental Health, in addition to health administrative data sources in Ontario. Canadian evidence suggests the prevalence of mood or anxiety disorders was consistently lower among migrant groups compared to estimates from the general population. Our findings suggest there was low concordance between survey and administrative data derived estimates of mood or anxiety disorders among migrant and non-migrant groups. Our Bayesian analysis suggests that the true prevalence of mood or anxiety disorders may lie between estimates derived from administrative and survey data. Our findings also indicate that the relationship between migration and mood or anxiety disorders is variable depending on migrant specific risk factors including migrant class and region of birth. Our work highlights the importance of contextualizing population-level data sources to accurately inform policy.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".