Above chaos, quest, and restitution: narrative experiences of African immigrant youth’s settlement in Canada
Bibliographic record
Abstract
BACKGROUND: African Immigrant and refugee youth represent an increasing group of newcomers in Canada. Upon their immigration, youth experience challenges that have the potential to lead to poor health, yet little is known about their settlement journey. Accordingly, this qualitative study examines the settlement journey of African immigrant and refugee youth with a focus on how their experiences were shaped by the social determinants of health. METHODS: We conducted a total of 70 interviews with 52 immigrant and refugee youth (ages 13-29 years) who had arrived in Canada in the preceding six years. Qualitative data was analyzed using Frank's dialogical narrative analysis approach (Frank AW, Practicing Dialogical Narrative Analysis. In: Varieties of Narrative Analysis, 2016). RESULTS: Youth experienced different settlement journeys that are described in the three narrative typologies of chaos, quest, and restitution. The chaos narrative of a long road ahead revealed the themes of 'facing challenges' and 'still the outsider.' The quest narrative of not there yet was represented by the themes of 'stepping out of your comfort zone' and 'being relentless.' The theme of 'supportive environments' depicted the restitution narrative of dreams become a reality. Youth highlighted the impact of social determinants of health in their settlement. CONCLUSION: Youth experienced different settlement journeys that were mired in chaos and challenges. However, youth were more likely to experience restitution when they received social support and found a sense of belonging. In future, policies and programs that seek to improve immigrant and refugee youth's settlement experiences need to be multifaceted, offer more support and promote a sense of belonging.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".