The mental health of immigrant and refugee children in Canada: A scoping review
Bibliographic record
Abstract
Introduction: First- and second-generation immigrant children under 15 years of age make up 37.5% of the total population of children in Canada. Immigrant children aged 10-19, irrespective of their immigration status, face more ethnic victimization at school and in their neighborhoods. By 2036, the number of immigrant children in Canada is predicted to increase by 49%. Method: A well-established Arksey and O’Malley’s five-stage methodological framework was applied to conduct this scoping review. This project reviewed the existing research literature on factors affecting immigrant and refugee children's mental health in Canada. Results: The mental health of immigrant and refugee children can be viewed as a combined product of personal, social, cultural, economic, and pre- and post-migratory factors. Immigrant and refugee children’s experiences of migration can be stressful and destabilizing. Service providers are not well trained and often cannot grasp the circumstances of immigrant and refugee children and families, which consequently disengages them from required treatment services and follow-up care. Conclusion: Reflection of diversity and inclusivity in mental health policies can influence actions in a primary care setting and reduce accessibility gaps and barriers that affect immigrant and refugee children in Canada. Keywords: child; Canada; immigrant; mental health; refugee
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".