Exploring the Impact of Resettlement on the Mental Health of Refugee Youths in Canada
Bibliographic record
Abstract
Refugee youths represent a growing demographic in Canada; highly vulnerable and constitutive of a population in need of better mental health support, post-resettlement. Accordingly, this research adopted a case study design to understand the impact of resettlement on the mental health of refugee youths through an exploration of their lived experiences. Data collection utilized a multi-method approach and included semi-structured interviews supplemented by participant-employed photography. The five participants who formed the sample were first-generation refugee youths between 15 and 24 years old who had been living in Canada for at least three years prior to this study. Through hermeneutic analysis, the data revealed that refugee youths tend to encounter mental health implications like spatial identity, survivor guilt, and emotional turmoil. Intriguingly, the data also revealed notions of cultural anosognosia which emerged through an amalgamation of studies within the disciplines of health sciences and anthropology. Thus, cultural anosognosia is presented in this study to describe the youths’ lack of insight or awareness of mental health concerns due to cultural upbringing. This research, therefore, highlights that whilst the challenges of resettlement are not collectively understood, there is a strong assertion of its profound impacts on the mental health of refugee youths in Canada.
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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 | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".