Appropriateness of assessment and treatment interventions for refugees and immigrant children and youth with mental health issues from clinicians' perspectives
Bibliographic record
Abstract
The population of immigrants and refugees has drastically increased in Canada in the last few decades. We are currently facing a refugee crisis around the world. In 2015, Canada welcomed 25,000 refugees who have fled from war in their home countries. Refugee families and their children experience significant loss, trauma, and emotional upheaval during the immigration process and this may significantly impact their mental health. Therefore, refugee children and youth need mental health support that can meet their complex and multifaceted issues such as poverty, housing, employment, language and cultural barriers, others.\n\nThis study used an exploratory, qualitative, cross-sectional, inductive research design. The data were collected through five semi-structured interviews using the general interview guide approach. The study explores from the clinicians’ perspective of the cultural appropriateness of assessment and treatment interventions for refugee and immigrant children and youth with mental health issues. The findings point to gaps in mental health services that may create barriers and prevent immigrant and refugee children from accessing appropriate and effective mental health treatment.\n\nFindings from the study indicate that refugee and immigrant children and youth have multilayered issues that need to be addressed holistically. Child and Youth mental health services can improve their services to be culturally appropriate by providing health care providers with cross-cultural training and adequate resources that can meet their client’s specific needs.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".