Asking Youth: Understanding the Sociocultural Factors that Impact Immigrant Youth Mental Health
Bibliographic record
Abstract
Introduction: Immigrant and refugee youth comprise one of the largest growing groups in Canada. Studies have shown that immigrant and refugee youth are especially vulnerable to psychosocial problems and deal with unmet health needs. However, the unique health challenges immigrant and refugee youth face remain largely understudied. This study aimed to better understand the social and cultural factors that impact the mental health of immigrant youths. Methods: We conducted 3 semi-structured focus group discussions with 15 high school immigrant youths (14-18 years old, 9 female and 6 male), of African, Asian, and Middle Eastern ethnicities. All participants were part of the Refugee and Immigrant Self-Empowerment (RISE) for Health and Wellness, a community youth engagement for health promotion program in Calgary. Deductive thematic analysis was performed. We used NVivo Software for the analysis process. Results: The participants’ mental health was primarily affected by external stressors of cultural stigma, parental expectations, and a lack of mental health knowledge. Parental pressure was a significant factor in impacting mental health due to cultural and intergenerational differences. Parental tactics such as comparing hardships were common inhibitors of healthy discussions on mental health. Conclusion: The feedback from the focus groups revealed the sociocultural factors that contribute to the mental health challenges of immigrant youth. Health promotion initiatives like RISE for Health are necessary to encourage conversations to help destigmatize mental health. While helpful in determining some situational factors, future focus groups should consider additional facets that could influence mental health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".