“You Have to be Resilient”: A Qualitative Study Exploring Advice Newcomer Youth Have for Other Newcomer Youth
Bibliographic record
Abstract
Abstract Research infrequently includes the perspectives of vulnerable and marginalized youth. As the population of newcomer youth in Canada continues to grow, it is imperative that attention is devoted not only to challenges they experience, but also to resilience factors they perceive to support their adjustment and well-being. To address this gap, this qualitative research explored newcomer youths’ experiences and advice for other newcomer youth who have recently arrived in Canada. Thirty-seven newcomer youth from two medium-sized cities in Ontario participated in focus groups. Participants ranged from 14 to 22 in age and identified mostly as female refugees from the Middle East. Through thematic analysis, five overarching themes were found across groups: (1) moving to a new country is hard, (2) maintain a healthy mindset, (3) take an active role in the adjustment process, (4) stay true to who you are, (5) and you are not alone. Youth described hardships that make moving to a new country difficult including lack of belonging due to racism and bullying, insufficient orientation to new systems, language barriers, and high levels of stress. Findings demonstrated youths’ resilience, coping skills, and strategies to lead meaningful lives. Youth discussed resilience strategies such as maintaining a connection with home culture and religion, reframing thinking to be positive, receiving emotional support, accessing community support at newcomer agencies, and building language proficiency. Findings provide implications for professionals working with newcomer youth and reflect the importance of addressing structural barriers and racism. The opportunity for newcomer youth to share experiences as experts in research may also help to promote resilience.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".