Predicting rejection attitudes toward utilizing formal mental health services in Muslim women in the US: Results from the Muslims’ perceptions and attitudes to mental health study
Bibliographic record
Abstract
Background: The underutilization of mental health services is a recognized problem for the growing number of Muslims living in the West. Despite their unique mental health risk factors and the pivotal role they play in determining mental health discourse in their families and in society, Muslim women in particular have not received sufficient study. Aim: To help remedy this research gap, we examined factors that may impact the rejection attitudes of Muslim women toward professional mental health care using the first psychometrically validated scale of its kind; the M-PAMH (Muslims’ Perceptions and Attitudes to Mental Health). Methods: A total of 1,222 Muslim women responded to questions about their cultural and religious beliefs about mental health, stigma associated with mental health, and familiarity with formal mental health services in an anonymous online survey. Results: Hierarchical multiple regression analysis revealed that higher religious and cultural beliefs, higher societal stigma, and lower familiarity with professional mental health services were associated with greater rejection attitudes toward professional mental healthcare. The final model was statistically significant, F (5, 1,216) = 73.778; p < .001, and explained 23% of the variance in rejection attitudes with stigma accounting for the most (12.3%) variance, followed by cultural and religious mental health beliefs (6%), and familiarity with mental health services (2.7%). Conclusions: Findings suggest that although the examined factors contributed significantly to the model, they may not be sufficient in the explanation of Muslim women's rejection attitudes toward mental health services. Future research may explore additional variables, as well as predictive profiles for Muslim women’s perceptions and attitudes of mental health based on a combination of these factors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".