Exploring the Mental Health and Cultural Identity of South Asian Immigrants in Ontario
Bibliographic record
Abstract
Many South Asians in Canada experience mental health challenges and report a lack of culturally sensitive support. Immigration is seen to be a key determinant in the mental health of individuals, and the acculturation process can significantly influence one’s cultural identity. This study seeks to examine the mental health and cultural identity of South Asian immigrants in Ontario through their immigration experience. Eight South Asian individuals who immigrated to Ontario within the past 15 years participated in a one-hour virtual semi-structured interview. They were asked about their immigration experiences and how these experiences affected their mental health, interpersonal relationships, and sense of cultural identity. The data was analyzed using thematic analysis and the aid of NVivo 15 to extract prevalent themes. The results suggested that many South Asian immigrants experience mental health challenges including anxiety and depressive symptoms, yet are met with a lack of accessible and culturally sensitive resources. Many participants described changes in their cultural identity. While some experienced initial discrimination or challenges adjusting to a new culture, most reported that they now see their identity as a balance between South Asian and Canadian. Among the three men and five women interviewed, no significant gender differences were observed, however the age at which one immigrated may appear to play a role in how willing they are to accept Western culture, as well as their desire to stay in Canada or move back home. This study highlights the importance of implementing culturally sensitive mental health care that is more readily available to South Asian immigrants, as well as additional resources that can help facilitate the immigration and acculturation experience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| 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".