Mental health and neurocognitive disorder–related hospitalization rates in immigrants and Canadian-born population: a linkage study
Bibliographic record
Abstract
OBJECTIVES: Mental health and neurocognitive conditions are important causes of hospitalization among immigrants, though patterns may vary by immigrant category, world region of origin, and time since arrival in Canada. This study uses linked administrative data to explore differences in mental health hospitalization rates between immigrants and individuals born in Canada. METHODS: Hospital records from the Discharge Abstract Database and the Ontario Mental Health Reporting System for 2011 to 2017 were linked to the 2016 Longitudinal Immigrant Database and to Statistics Canada's 2011 Canadian Census Health and Environment Cohort. Age-standardized hospitalization rates for mental health-related conditions (ASHR-MHs) were derived for immigrants and the Canadian-born population. ASHR-MHs overall and for leading mental health conditions were compared between immigrants and the Canadian-born population, stratified by sex and selected immigration characteristics. Quebec hospitalization data were not available. RESULTS: Overall, immigrants had lower ASHR-MHs compared to the Canadian-born population. Mood disorders were leading causes of mental health hospitalization for both cohorts. Psychotic, substance-related, and neurocognitive disorders were also leading causes of mental health hospitalization, although there was variation in their relative importance between subgroups. Among immigrants, ASHR-MHs were higher among refugees and lower among economic immigrants, those from East Asia, and those who arrived in Canada most recently. CONCLUSION: Differences in hospitalization rates among immigrants from various immigration streams and world regions, particularly for specific types of mental health conditions, highlight the importance of future research that incorporates both inpatient and outpatient mental health services to further understand these relationships.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".