Perceived Islamophobia and Psychological Distress Among Muslim Immigrants in Canada: The Moderating Role of Group Identification
Bibliographic record
Abstract
Abstract The purpose of this quantitative correlational study with moderation was to examine the differing moderating roles of centrality and in-group superiority in the relationship between perceived Islamophobia and psychological distress. Perceived Islamophobia, group centrality, in-group superiority, and psychological distress were measured using Perceived Islamophobia Scale, the shorter version of the Identity Centrality Scale , Perceived In-Group Superiority Scale , and Kessler Psychological Distress Scale. In this study, a convenience sample (N = 113) of Muslim males and females above 18 years old was used. An online survey tool, SurveyMonkey, was used to collect data from Muslim immigrants living in Calgary, Canada. The analytical strategy was to conduct 2 separate hierarchical moderated regression analyses (1 for identity centrality and 1 for in-group superiority) to examine the moderating role group identity. Social identity theory provided the theoretical foundation to answer the question of how perceived Islamophobia impacts the psychological distress of Muslim immigrants in Canada. The findings indicated that perceived Islamophobia significantly predicts psychological distress among Muslim immigrants in Canada, and identity centrality significantly moderates the relationship between perceived Islamophobia and psychological distress by buffering against the negative effects of percieved group discrimination. However, in group superiority was not a significant moderator in the relationship between perceived Islamophobia and psychological distress.The findings will be beneficial for the practitioners and policy makers to devise better intervention strategies for the wll-being of muslim immigrants in Canda to bring a positive social change in society.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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".