Voices of South Asian Women: Immigration and Mental Health
Bibliographic record
Abstract
PURPOSE: This qualitative research aimed to elicit experiences and beliefs of recent South Asian immigrant women about their major health concerns after immigration. METHODS: Four focus groups were conducted with 24 Hindi-speaking women who had lived less than five years in Canada. The audiotaped data were transcribed, translated, and analyzed by identification of themes and subcategories. RESULTS: Mental health (MH) emerged as an overarching health concern with three major themes i.e. appraisal of the mental burden (extent and general susceptibility), stress-inducing factors, and coping strategies. Many participants agreed that MH did not become a concern to them until after immigration. Women discussed their compromised MH using verbal and symptomatic expressions. The stress-inducing factors identified by participants included loss of social support, economic uncertainties, downward social mobility, mechanistic lifestyle, barriers in accessing health services, and climatic and food changes. Women's major coping strategies included increased efforts to socialize, use of preventative health practices and self-awareness. CONCLUSION: Although participant women discussed a number of ways to deal with post-immigration stressors, the women's perceived compromised mental health reflects the inadequacy of their coping strategies and the available resources. Despite access to healthcare providers, women failed to identify healthcare encounters as opportunities to seek help and discuss their mental health concerns. Health and social care programs need to actively address the compromised mental health perceived by the studied group.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".