Rates of Mental Illness and Suicidality in Immigrant, Refugee, Ethnocultural, and Racialized Groups in Canada: A Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: Studies from around the world point to differences in the rates of mental illnesses between immigrant, refugee, ethnocultural, and racialized (IRER) groups and host populations. Risk of illness depends on social contexts; therefore, to offer the best information for people aiming to develop and offer equitable services, local information on rates of mental illness in different population groups is required. METHODS: We performed a literature review of peer-reviewed journals and the grey literature between 1990 and 2009 using standard techniques and identified primary research reporting the rates of mental illness and suicidality in IRER groups in Canada. RESULTS: Among the 229 papers we reviewed, 17 were included. Most papers reported rates for depression. There was no clear pattern, with different IRER groups and different age groups reporting either elevated or lower rates, compared with white Canadians. Refugee youth in Quebec have higher rates of numerous mental health problems and illnesses. When immigrant groups were considered as a whole, suicide rates were low but different national origin groups reported different trajectories in rates across the generations. CONCLUSION: The literature on rates of mental illness and suicidality in IRER groups in Canada is diverse and not comprehensive. In addition, most research has been conducted in 3 provinces and, in particular, 3 major cities. The rates of mental illness seem to vary by national origin groups, age, and status in Canada. There is very little research on nonimmigrant, culturally diverse populations in Canada. This lack of information may undermine efforts to develop equitable mental health services for all Canadians.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.031 | 0.047 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".