Variations in Conduct, Attention Deficit Hyperactivity Disorder, Mood and Anxiety Disorders Among Children and Youth from Immigrant, Refugee, and Non-Immigrant Backgrounds in British Columbia, Canada: A Population-Based Study
Bibliographic record
Abstract
Despite growing attention to child and youth mental health, knowledge gaps exist related to how mental disorders vary for children and youth from diverse backgrounds. The purpose of the present study was to investigate how conduct, attention deficit hyperactivity disorder (ADHD), and mood/anxiety diagnoses varied by immigrant, refugee, and non-immigrant background in British Columbia, Canada. The study utilized population-based, linked administrative data for nearly half a million children and youth (N = 470,464) between 1996 and 2016 (ages 3 to 19) to examine variations in mental disorder diagnosis (defined via administrative health data records) by immigrant generation and admission category (economic, family, refugee) and the predictive/moderating effects of key socio-demographic factors (e.g., sex, socioeconomic status). Findings indicated that first- and second-generation children and youth were less likely to receive a mental disorder diagnosis compared to non-immigrant children and youth. Those in the refugee admission category had higher odds of conduct and mood/anxiety disorder diagnosis and those in the family admission category had higher odds of conduct, ADHD, and mood/anxiety disorder diagnosis (versus the economic admission category). Significant interactions revealed that sex at birth and socioeconomic status differently predicted mental disorder diagnoses for children and youth from immigrant and refugee backgrounds (versus non-immigrant). The findings contribute to a more nuanced understanding of mental disorder diagnoses for children and youth from diverse backgrounds and that well-established predictors of mental disorders for the general population (i.e., sex, SES) differ for children and youth from immigrant and refugee backgrounds.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".