Mental Health of Immigrant Children and Adolescents (6–17 Years) in Canada: Evidence from the Canadian Health Measures Survey
Bibliographic record
Abstract
BACKGROUND: Studies indicate a higher prevalence of mental health problems among immigrants, but findings on immigrant children and adolescents are mixed. We sought to understand the magnitude of differences in mental health indicators between immigrant and non-immigrant children and adolescents in Canada and the influence of age, sex, household income, and household education. METHODS: We completed a secondary analysis of data from the Canadian Health Measures Survey, using a pooled estimate method to combine data from four survey cycles. A weighted logistic regression was used to estimate the unadjusted and adjusted odds ratios with 95% confidence intervals. RESULTS: We found an association between the mental health of immigrant versus non-immigrant children and adolescents (6-17 years) as it relates to emotional problems and hyperactivity. Immigrant children and adolescents had better outcomes with respect to emotional problems and hyperactivity/inattention compared to non-immigrant children and adolescents. Lower household socioeconomic status was associated with poorer mental health in children and adolescents. CONCLUSION: No significant differences in overall mental health status were evident between immigrant and non-immigrant children and adolescents in Canada but differences exist in emotional problems and hyperactivity. Sex has an influence on immigrant child mental health that varies depending on the specific mental health indicator.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".