Arab Muslims’ Explanatory Models of Mental Illness: A Mixed-Methods Inquiry Using Cultural Consensus Approach
Bibliographic record
Abstract
Arab Muslims are one of the fastest growing populations in Canada. Similar to other ethnocultural minority groups, Arab Muslims report high rates of psychological distress compared to the general population. Despite these high rates, however, Arab Muslims tend to use mental health services less than the general population, entering treatment only when the symptoms have become severe and terminating treatment prematurely. This series of studies aimed to better understand the cultural models of mental illness and help-seeking used by Arab Muslims in Canada, focusing especially on aspects of these models that might contribute to social disparities in access to mental health care. To this end, three studies were conducted in the Montreal community following the sequential cultural consensus approach. In study 1, 54 Arab Muslims free listed (a) key Muslim values; (b) beliefs about mental illness; and (c) help-seeking strategies. Items listed by more than 10% of participants were used in the following study. In study 2, 40 Arab Muslims completed three pile-sorting tasks while speaking aloud as they sorted previously identified salient concepts. Qualitative and multidimensional scaling methods were used to characterize the piles. In study 3, 68 Arab Muslims completed a questionnaire designed based on results from the first two studies. Cultural consensus analysis was used to determine the degree to which participants agreed or disagreed with the identified cultural model. The extent to which endorsement of different aspects of the shared model influenced help-seeking preferences was also evaluated. Taken together, results confirmed that there is strong consensus around key Muslim values. However, individuals may emphasize some aspects of the model more than others. Additionally, results point to variations in beliefs about causes of mental illness, with some participants emphasizing biopsychosocial and some emphasizing spiritual causes of mental illness. Results showed that these variations have impact on help-seeking preferences. Overall, participants indicated higher willingness to use professional mental health services than anticipated, albeit with significant concerns about whether their religious ideas would be acceptable to these professionals. The usefulness of the mixed methods cultural consensus approach to cultural-clinical psychology questions is discussed.
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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 | 0.048 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".