An Intersectional Approach to Understanding Beliefs and Attitudes Toward Mental Health Issues Among Muslim Immigrant Women in Canada
Bibliographic record
Abstract
Although addressing cultural and religious practices is important in providing mental health care, little research exists on understanding mental health issues of minority groups such as Muslim immigrant women. We employed an intersectional approach to examine beliefs and attitudes toward mental health issues among Muslim immigrant women in Canada. Four focus groups (21 participants) were conducted, and 101 surveys were collected in Ottawa, Canada. Three core themes emerged from thematic content analysis of focus group data that relate to participants' communication about: 1) stressors, 2) mental health care seeking, and 3) utilizing coping strategies. The survey data were analyzed using independent samples t-test and One-Way ANOVA, the results of which supported the qualitative findings that social stigma was an important obstacle preventing those women from seeking professional mental health services. Muslim women with South and Southeast Asian cultural/ethnic backgrounds were more likely to get help from professionals than those with African cultural/ethnic backgrounds. No group differences were found in age, family income, and employment status. Broadly, the findings underscore the importance of developing knowledge about the intersections among gender, religion, cultural identity, immigration status, and social stigma that influence beliefs and attitudes toward mental health issues. Specifically, the findings point to the need for an intersectional approach that offers a more nuanced understanding for tailoring mental health care to Muslim immigrant women's needs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".