Identifying Mental Health Issues In Canadian Immigrants With A Focus On Indian Immigrants
Bibliographic record
Abstract
Purpose: The purpose of this study is to expand on current knowledge of the mental health of immigrants in Canada by identifying differences in self-rated mental health among immigrant groups based on immigration related, sociodemographic and health and healthcare access related characteristics. Methods: Data from the 2015/2016 and 2017/2018 cycles of the Canadian Community Health Survey (CCHS), an annual cross-sectional survey disseminated by Statistics Canada, were accessed. Analysis includes descriptive statistics, and a bivariate analysis of each independent variable with the dependent variables. Three sets of series of logistic regression models were analyzed to assess associations between these variables and 1) self-reported mental health (good/poor); 2) reporting a mood disorder (no/yes); and, 3) having an anxiety disorder (no/yes). Results: Results provide support for the healthy immigrant effect. Immigrating in later life and from a lower income country is advantageous for mental health. Living in urban areas, being white, having a higher income, not being under the age of 12 or over 65, are all associated with better mental health in immigrants. Conclusions: Certain sociodemographic and immigration related characteristics put immigrants at risk of poorer mental health outcomes. Further research is necessary to further elucidate these relationships and how mental health supports can be targeted to those who may be at the greatest risk post immigration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 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) | 0.003 | 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".