Suicidal Ideation and Healthy Immigrant Effect in the Canadian Population: A Cross-Sectional Population Based Study
Bibliographic record
Abstract
Understanding suicidal ideation is crucial for preventing suicide. Although “healthy immigrant effect” is a phenomenon that has been well documented across a multitude of epidemiological and social studies—where immigrants are, on average, healthier than the native-born, little research has examined the presence of such effect on suicidal ideation. The objective of this study is to investigate if there is a differential effect of immigration identity on suicidal ideation and how the effect varies by socio-demographic characteristics in the Canadian population. Data from the Canadian Community Health Survey in year 2014 were used. Multivariate logistic regression was employed. Our findings indicated that recent immigrants (lived in Canada for 9 or less years) were significantly less likely to report suicidal ideation compared with non-immigrants. However, for established immigrants (10 years and above of living in Canada), the risk of suicidal ideation converged to Canadian-born population. Moreover, male immigrants were at significantly lower risk of having suicidal ideation than Canadian-born counterparts; whereas, female immigrants did not benefit from the “healthy immigrant effect”. Our findings suggest the need for targeted intervention strategies on suicidal ideation among established immigrants and female immigrants.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".