A Qualitative Study of the Process of Acculturation and Coping for South Asian Muslim Immigrants Living in the Greater Toronto Area (GTA)
Bibliographic record
Abstract
The present study explores the nature of coping mechanisms among South Asian Muslim immigrants living in the Greater Toronto Area (GTA) who have been living in Canada between three to five years and experienced acculturation challenges and depression. Thirteen immigrants (seven females and six males) were interviewed to share their stories of personal experiences of settlement and acculturation in Canada. These interviews were analyzed using the grounded theory approach to develop themes and sub-themes to understand and interpret the data. The findings reveal that the research participants experienced a number of acculturation challenges (feeling different, feeling excluded, disruption in the family and material differences) which led to depression. During the course of their depression participants experienced certain events which became turning points in their lives, subsequently motivating them to change the way in which they live. They sought out particular kinds of support and coping mechanisms which helped them to settle, integrate and belong to the Canadian culture. The midlevel grounded theory that has emerged from participants’ responses is discussed. Recommendations are made to inform mental health professionals to incorporate these coping mechanisms in delivering culturally sensitive services to the target population. Study implications for theory, psychotherapy, counselling and other mental health practices and future research in the area of settlement and adaption of newcomers in Canada are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".