Racial/Ethnic Disparities in Psychiatric Traits and Diagnoses within a Community-based Sample of Children and Youth: Disparités raciales/ethniques dans les traits et diagnostics psychiatriques au sein d’un échantillon communautaire d’enfants et de jeunes
Bibliographic record
Abstract
Objective Racial/ethnic disparities in the prevalence of psychiatric disorders have been reported, but have not accounted for the prevalence of the traits that underlie these disorders. Examining rates of diagnoses in relation to traits may yield a clearer understanding of the degree to which racial/ethnic minority youth in Canada differ in their access to care. We sought to examine differences in self/parent-reported rates of diagnoses for obsessive-compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD) and anxiety disorders after adjusting for differences in trait levels between youth from three racial/ethnic groups: White, South Asian and East Asian. Method We collected parent or self-reported ratings of OCD, ADHD and anxiety traits and diagnoses for 6- to 17-year-olds from a Canadian general population sample (Spit for Science). We examined racial/ethnic differences in trait levels and the odds of reporting a diagnosis using mixed-effects linear models and logistic regression models. Results East Asian ( N = 1301) and South Asian ( N = 730) youth reported significantly higher levels of OCD and anxiety traits than White youth ( N = 6896). East Asian and South Asian youth had significantly lower odds of reporting a diagnosis for OCD (odds ratio [ OR ] East Asian = 0.08 [0.02, 0.41]; OR South Asian = 0.05 [0.00, 0.81]), ADHD ( OR East Asian = 0.27 [0.16, 0.45]; OR South Asian = 0.09 [0.03, 0.30]) and anxiety ( OR East Asian = 0.21 [0.11, 0.39]; OR South Asian = 0.12 [0.05, 0.32]) than White youth after accounting for psychiatric trait levels. Conclusions These results suggest a discrepancy between trait levels of OCD, ADHD and anxiety and rates of diagnoses for East Asian and South Asian youth. This discrepancy may be due to increased barriers for ethnically diverse youth to access mental health care. Efforts to understand and mitigate these barriers in Canada are needed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".