Using Youth Voice to Inform Programs and Services Promoting Newcomers' Healthy Development
Bibliographic record
Abstract
Effective programs to support the healthy development and well-being of youth who have immigrated to Canada are needed. This integrated-article dissertation accentuated the perspectives of refugee and immigrant youth to identify considerations for programming and strategies to promote their healthy development. The first paper (Chapter Two) utilized focus groups to explore newcomer youths’ experiences relocating to a new country and advice for other youth who have recently arrived in Canada. We identified five overarching themes across groups through thematic analysis: (1) moving to a new county is hard, (2) maintain a healthy mindset, (3) take an active role in the adjustment process, (4) stay true to who you are, and (5) you are not alone. Findings captured the hardships of adapting to a new country while also demonstrating participants’ resilience, coping skills, and strategies to lead meaningful lives.\nThe second paper (Chapter Three) utilized youth voice to identify considerations for developing programming to support newcomer youths’ relationships and well-being. We applied group concept mapping and identified six concepts as follows, in rank order of importance: create a space for sharing; discuss relational issues; teach strategies for adjusting to a new country; teach skills for wellness; have feel-good activities; and plan for diversity. Participants’ lived experience and their own attendance in programming at newcomer-serving organizations provided a basis for them to brainstorm what types of activities, topics and skills they believe would be helpful for other newcomer youth, as well as considerations for facilitators implementing such programming.\nFinally, the third paper (Chapter Four) evaluated the acceptability of an evidence-informed healthy relationships program with newcomer youth at three newcomer-serving agencies. Using a mixed-methods case study approach, the perspectives of youth participants, program facilitators, and agency administrators suggested the program is promising in terms of fit and acceptability. Stakeholders also identified how facilitators can tailor content and activities to be more accessible and culturally meaningful for immigrants and refugees. Taken together, the findings from these papers highlight the perspectives of newcomer youth and advance understanding of how to support their adjustment and healthy development.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".