Ethnic Differences in Mental Illness Severity
Bibliographic record
Abstract
OBJECTIVE: Little is known about the sociocultural determinants of mental illness at hospital presentation. Our objective was to examine ethnic differences in illness severity at hospital admission among Chinese, South Asian, and the general population living in Ontario, Canada. METHODS: We conducted a large, population-based, cross-sectional study of psychiatric inpatients aged from 19 to 105 years who were discharged between 2006 and 2014. A total of 133,588 patients were classified as Chinese (n = 2,582), South Asian (n = 2,452), or the reference group (n = 128,554) using a validated surnames algorithm (specificity: 99.7%). Diagnoses were based on DSM-IV criteria. We examined the association between ethnicity and 4 measures of disease severity: involuntary admissions, aggressive behaviors, and the number and frequency of positive symptoms (ie, hallucinations, command hallucinations, delusions, and abnormal thought process) (Positive Symptoms Scale, Resident Assessment Instrument-Mental Health [RAI-MH]). RESULTS: After adjusting for sociodemographic characteristics, immigration status, and discharge diagnosis, Chinese patients had greater odds of involuntary admissions (odds ratio [OR] = 1.79; 95% CI, 1.64-1.95) and exhibiting severe aggressive behaviors (OR = 1.36; 95% CI, 1.23-1.51) and ≥ 3 positive symptoms (OR = 1.39; 95% CI, 1.24-1.56) compared to the general population. South Asian ethnicity was also an independent predictor of most illness severity measures. The association between Chinese ethnicity and illness severity was consistent across sex, diagnostic and immigrant categories, and first-episode hospitalization. CONCLUSIONS: Chinese and South Asian ethnicities are independent predictors of illness severity at hospital presentation. Understanding the role of patient, family, and health system factors in determining the threshold for hospitalization is an important future step in informing culturally specific care for these large and growing populations worldwide.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".