Trends in mental health service utilisation in immigrant youth in Ontario, Canada, 1996–2012: a population-based longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVE: To describe trends in mental health service use of youth by immigration status and characteristics. DESIGN: Population-based longitudinal cohort study from 1996 to 2012 using linked health and administrative datasets. SETTING: Ontario, Canada. PARTICIPANTS: Youth 10-24 years, living in Ontario, Canada. EXPOSURE: The main exposure was immigration status (recent immigrants vs long-term residents). Secondary exposures were region of origin and refugee status. MAIN OUTCOME MEASURE: Mental health hospitalisations, emergency department (ED) visits and outpatient visits within consecutive 3-year time periods. Poisson regression models estimated rate ratios (RR). RESULTS: Over 2.5 million person years per period were included. Rates of recent immigrant mental health service utilisation were at least 40% lower than long-term residents (p<0.0001).Mental health hospitalisation and ED visit rates increased in long-term residents (hospitalisations, RR 1.09 (95% CI 1.08 to 1.09); ED visits, RR 1.15 (1.14 to 1.15)) and recent immigrants (hospitalisations RR 1.05 (1.03 to 1.07); ED visits, RR 1.08 (1.05 to 1.11)). Mental health outpatient visit rates increased in long-term residents (RR 1.03 (1.03 to 1.03)) but declined in recent immigrant (RR 0.94 (0.93 to 0.95)). Comparable divergent trends in acute care and outpatient service use were observed among refugees and across most regions of origin. Recent immigrant acute care use was driven by longer-term refugees (hospitalisations RR 1.12 (1.03 to 1.21); ED visits RR 1.11 (1.02 to 1.20)). CONCLUSIONS: Mental health service utilisation was lower among recent immigrants than long-term residents. While acute care use is increasing at a faster rate among long-term residents than recent immigrants, the decrease in outpatient mental health visits in immigrants highlights a potential emerging disparity in access to preventative care.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".