Rethinking resilience: a regression analysis study of the experiences of refugee and immigrant youth in Montreal
Bibliographic record
Abstract
Purpose Refugee and immigrant youth (RIY) experience multifaceted challenges, but also have the potential to become resilient. Most of the existing literature focuses on the challenges these RIY face with limited attention to their agency and resilience. This study aims to assess the factors that predict RIY’s resilience among refugee and immigrant youth in Montreal, Canada. Design/methodology/approach A sample of 93 RIY in Montreal was surveyed. A questionnaire consisting of validated scales was used for data collection. Findings The study found a positive correlation between educational level, personal resilience and relational resilience (p < 0.001). However, ethnicity did not have a significant correlation with the participant’s general level of resilience (p > 0.001). Cultural, religious, family, community ties, age and time lived in Montréal were found to be predictors of general resilience, relational resilience and personal resilience of the RIY (p < 0.001). Originality/value The study concluded that factors such as cultural, religious and community ties are major predictors of the resilience of RIY in Montreal. Hence, the need for mental health practitioners and resettlement organizations that work with RIY to focus on reconceptualizing resilience to incorporate the cultural, religious and community ties of RIY. This will help in developing services and programs that are culturally sensitive and effective in fostering the resilience of RIY.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".