Identifying Mental Health Issues in Indian Immigrants in Canada: A Comparison with Non-Indian Immigrants
Bibliographic record
Abstract
Much of the literature on the mental health of immigrants tends to generalize, treating all immigrants as one category, and not accounting for how life experiences in the country of origin can shape mental health. Therefore, the purpose of this study is to contrast the differences in self-rated mental health between Indian immigrants and non-Indian immigrants based on immigration-related factors, sociodemographic factors and health and healthcare utilization-related factors. Cross-sectional data from two cycles of the Canadian Community Health Survey were analyzed. Logistic regression models were analyzed to assess self-reported mental health and those reporting a mood or anxiety disorder. Results provide support for the healthy immigrant effect and find that immigrating in later life is advantageous for mental health for Indian immigrants. Having a lower income, a smaller household, and living in a rural area are associated with good mental health among Indian immigrants, but not among all immigrants. Being male does not have the same protective effect against mental health concerns in Indian immigrants as it does in all immigrants. Results demonstrate the need to study immigrant groups by their country of origin and how life experiences in a particular country shape immigrant mental health differently from country to country.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".