Understanding the mental health experiences of west African Canadian immigrants
Bibliographic record
Abstract
The mental health of immigrants is a growing concern globally, with limited research focusing on West African immigrants in Canada. The present study aimed to examine mental health experiences among West African immigrants in Canada. The study employed a quantitative approach recruiting 54 West African immigrants completing an online survey and 7 participants engaging in semi-structured interviews. Descriptive statistics, correlation, and regression analysis were utilized to analyze the data. The study found that 92.6% of participants rated their mental health as healthy before migrating to Canada. However, after migration, the proportion of participants reporting positive mental health decreased to 59.3%, with 31.5% at risk and 9.3% unhealthy. Career change, acculturation stress, migration stress, cultural differences, and unavailability of mental health services were reported as factors that affected mental health. The study revealed a decline in mental health status among West African immigrants in Canada after migration, with a need for culturally appropriate mental health services. Mental health service providers need to be aware of the diverse attitudes towards mental health services to improve utilization among West African immigrants. The study shows that the mental health of West African Canadian immigrants declines upon immigrating to Canada and there is a need for culturally appropriate mental health services for the population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".