“Just as Canadian as Anyone Else”? Experiences of Second-Class Citizenship and the Mental Health of Young Immigrant and Refugee Men in Canada
Bibliographic record
Abstract
In recent years, the experiences of immigrant and refugee young men have drawn attention worldwide. Human-induced environmental disasters, local and global conflicts, and increasingly inequitable distributions of wealth have shaped transnational migration patterns. Canada is home to a large immigrant and refugee population, particularly in its urban areas, and supporting the mental health and well-being of these communities is of critical importance. The aim of this article is to report findings from a qualitative study on the social context of mental health among immigrant and refugee young men, with a focus on their migration and resettlement experiences. Informed by the conceptual lens of social context, a thematic narrative analysis approach was used to examine qualitative data from individual and group interviews with 33 young men (age 15 to 22 years) self-identified as immigrants or refugees and were living in Greater Vancouver, western Canada. Three thematic narratives were identified: a better life, living the (immigrant) dream, and starting again from way below. The narratives characterized the social context for immigrant and refugee young men and were connected by a central theme of negotiating second-class citizenship. Implications include the need for mental health frameworks that address marginalization and take into account the contexts and discourses that shape the mental health of immigrant and refugee populations in Canada and worldwide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".