Mental Illness Stigma Among Middle Eastern Canadians: A Mixed Methods Study
Bibliographic record
Abstract
Mental illness stigma (MIS) is a global phenomenon, which perpetuates the distress associated with the symptoms of mental illness by acting as a deterrent to treatment (Tzouvara, Papadopoulos, & Randhawa, 2016). Research has highlighted disparities in the expression of MIS cross-culturally. However, little is known about MIS in the Middle Eastern Canadian (MEC) context. To address this gap in the literature, the current study employed a concurrent mixed methods design to assess the impact and explore the nature of MIS among Middle Eastern relative to White, Black, and South Asian Canadians (n = 424). A hierarchical regression analysis was performed to determine whether the perception of MIS in ones ethno-racial community acts as a greater deterrent to help-seeking in Middle Eastern versus White participants, after controlling for social desirability, familiarity with mental illness, and degree of identification with ones ethno-racial group. A second set of hierarchical regression analyses, alongside a qualitative content analysis, were used to explore the nature of MIS among MEC. As for the impact of MIS, results showed that perceived public MIS was a greater deterrent to help-seeking among MEC than it was among those identifying as White. No differences were found between the Middle Eastern and the South Asian or Black groups. In terms of the nature of MIS, quantitative findings suggested that MEC endorsed higher levels of anxiety and social distance, both proxies for MIS, than White and Black Canadian groups respectively. In all cases, the effect of ethno-racial group on MIS was small. Between group differences on six other subscales assessing prejudice toward persons affected by mental illness (PABMI) were not significant after accounting for the effect of familiarity with mental illness on the dependent variables. Qualitative findings extended these results by highlighting other stereotypes about PABMI endorsed by MEC, not captured in the quantitative measures, namely, that PABMI are inadequate, crazy, different, a failure and a nuisance, and that their experience is invalid. Findings underscore the importance of incorporating contact with PABMI in anti-stigma campaigns, and of adapting these to the stereotypes about PABMI commonly held by members of a particular ethno-racial group
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".