Risk of involuntary admission among first-generation ethnic minority groups with early psychosis: a retrospective cohort study using health administrative data
Bibliographic record
Abstract
AIMS: Ethnic minority groups often have more complex and aversive pathways to mental health care. However, large population-based studies are lacking, particularly regarding involuntary hospitalisation. We sought to examine the risk of involuntary admission among first-generation ethnic minority groups with early psychosis in Ontario, Canada. METHODS: Using health administrative data, we constructed a retrospective cohort (2009-2013) of people with first-onset non-affective psychotic disorder aged 16-35 years. This cohort was linked to immigration data to ascertain migrant status and country of birth. We identified the first involuntary admission within 2 years and compared the risk of involuntary admission for first-generation migrant groups to the general population. To control for the role of migrant status, we restricted the sample to first-generation migrants and examined differences by country of birth, comparing risk of involuntary admission among ethnic minority groups to a European reference. We further explored the role of migrant class by adjusting for immigrant vs refugee status within the migrant cohort. We also explored effect modification of migrant class by ethnic minority group. RESULTS: We identified 15 844 incident cases of psychotic disorder, of whom 19% (n = 3049) were first-generation migrants. Risk of involuntary admission was higher than the general population in five of seven ethnic minority groups. African and Caribbean migrants had the highest risk of involuntary admission (African: risk ratio (RR) = 1.52, 95% CI = 1.34-1.73; Caribbean: RR = 1.58, 95% CI = 1.37-1.82), and were the only groups where the elevated risk persisted when compared to the European reference group within the migrant cohort (African: RR = 1.24, 95% CI = 1.04-1.48; Caribbean: RR = 1.29, 95% CI = 1.07-1.56). Refugee status was independently associated with involuntary admission (RR = 1.16, 95% CI = 1.02-1.32); however, this risk varied by ethnic minority group, with Caribbean refugees having an elevated risk of involuntary admission compared with Caribbean immigrants (RR = 1.72, 95% CI = 1.15-2.58). CONCLUSIONS: Our findings are consistent with the international literature showing increased rates of involuntary admission among some ethnic minority groups with early psychosis. Interventions aimed at improving pathways to care could be targeted at these groups to reduce disparities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".