Suicide and Self-Harm Among Immigrant Youth to Ontario, Canada From Muslim Majority Countries: A Population-Based Study
Bibliographic record
Abstract
ObjectiveTo examine the association between Muslim religious affiliation and suicide and self-harm presentations among first- and second-generation immigrant youth.MethodsWe performed a population-based cohort study involving individuals aged 12 to 24 years, living in Ontario, who immigrated to Canada between 1 January 2003 and 31 May 2017 (first generation) and those born to immigrant mothers (second generation). Health administrative and demographic data were used to analyze suicide and self-harm presentations. Sex-stratified logistic regression models generated odds ratios (OR) for suicide and negative binomial regression models generated rate ratios (aRR) for self-harm presentations, adjusting for refugee status and time since migration.ResultsOf 1,070,248 immigrant youth (50.1% female), there were 129,919 (23.8%) females and 129,446 (24.2%) males from Muslim-majority countries. Males from Muslim-majority countries had lower suicide rates (3.8/100,000 person years [PY]) compared to males from Muslim-minority countries (5.9/100,000 PY) (OR: 0.62, 95% CI, 0.42–0.92). Rates of suicide between female Muslim-majority and Muslim-minority groups were not different (Muslim-majority 1.8/100,000 PY; Muslim-minority 2.2/100,000 PY) (OR: 0.82, 95% CI, 0.46–1.47). Males from Muslim-majority countries had lower rates of self-harm presentations than males from Muslim-minority (<10%) countries (Muslim majority: 12.2/10,000 PY, Muslim-minority: 14.1/10,000 PY) (aRR: 0.82, 95% CI, 0.75, 0.90). Among female immigrants, rates of self-harm presentations were not different among Muslim-majority (30.1/10,000 PY) compared to Muslim-minority (<10%) (32.9/10,000 PY) (aRR: 0.93, 95% CI, 0.87–1.00) countries. For females, older age at immigration conferred a lower risk of self-harm presentations.ConclusionBeing a male from a Muslim-majority country may confer protection from suicide and self-harm presentations but the same was not observed for females. Approaches to understanding the observed sex-based differences are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".