From struggle to strength in African and Middle Eastern newcomers’ integration stories to Canada: A participatory health equity research study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Newcomers (immigrants, refugees, and international students) face many personal, gender, cultural, environmental and health system barriers when integrating into a new society. These struggles can affect their health and social care, reducing access to mental health care. This study explores the lived experiences of African and Middle Eastern newcomers to Ontario, Canada. An understanding of newcomer integration challenges, successes and social justice issues is needed to improve health equity and social services. METHODS: In this qualitative study, we used a participatory research approach to collect stories reflecting participants' integration perspectives and experiences. Beginning with our immigrant community network, we used snowball sampling to recruit newcomers, ages 18 to 30, originating from Africa or the Middle East. We used qualitative narrative analysis to interpret stories, identifying context themes, integrating related barriers and facilitators, and resolutions and learnings. We shared our findings and sought final feedback from our participants. FINDINGS: A total of 18 newcomers, 78% female and approximately half post-secondary students, participated in the study. Participants described an unknown and intimidating migration context, with periods of loneliness and isolation aggravated by cold winter conditions and unfamiliar language and culture. Amidst the struggles, the support of friends and family, along with engaging in schoolwork, exploring new learning opportunities, and participating in community services, all facilitated integration and forged new resilience. CONCLUSIONS: Community building, friendships, and local services emerged as key elements for future immigrant service research. Utilizing a participatory health research approach allowed us to respond to the call for social justice-oriented research that helps to generate scientific knowledge for promoting culturally adaptive health care and access for marginalized populations.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.050 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".