First- Generation Immigrants and Hospital Admission Rates for Psychosis and Affective Disorders: An Ecological Study in Ontario
Bibliographic record
Abstract
OBJECTIVE: The immigrant population in Canada, and particularly in Ontario, is increasing. Our ecological study first assessed if there was an association between areas with proportions of first-generation immigrations and admissions rates for psychotic and affective disorders. Second, this study examined if area-level risks would persist after controlling for area socioeconomic factors in census-derived geographical areas-Forward Sortation Areas (FSAs)-in Ontario. METHODS: Ontario's inpatient admission records from 1996 to 2005 and census data from 2001 were analyzed to derive FSA rates of first admissions for psychotic disorders and affective disorders per 100 000 person-years. Negative binomial regression models were adjusted, first, for FSA age and sex and, second, also for FSA population density and average income. RESULTS: Using age- and sex-adjusted models, admission rates for psychotic disorders were higher in areas with greater proportions of immigrants. These areas were associated with lower admission rates for affective disorders. When FSA average income and population density were added to the models, the influence of immigrants was attenuated to nonsignificant levels in models predicting psychotic disorders admission rates. However, greater proportions of immigrants remained significantly protective when predicting rates of affective disorders. DISCUSSION: Our study provides insight about the influence of area-level variables on risk of admission for psychotic and affective disorders in high immigrant areas. There is a dearth of current Canadian research on immigrant admission for psychotic disorders at the individual or area level. Future area- and individual-level studies may better identify groups at risk and possible explanations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".