A test of the internalized stigma mediation model
Bibliographic record
Abstract
Background. Muslims living in the West seek mental health services at lower rates than the general population despite facing difficulties which may require treatment. A barrier to treatment seeking may be stigma. General research on help-seeking suggests that public stigma, internalized as self-stigma, leads to negative attitudes and less favourable intentions toward seeking psychological help. However, there appear to be cross-cultural differences in this internalized stigma model, suggesting that acculturation and enculturation may moderate the model. Objectives. The first objective is to investigate the applicability of the internalized stigma model (Vogel, Wade, & Hackler, 2007) in a sample of Canadian Muslims. The second objective is to explore how acculturation and enculturation moderate the internalized stigma model. Methods. 238 Canadian Muslim participants completed an online survey that included measures of public and self-stigma of help-seeking, attitudes toward help-seeking, intentions to seek help, and acculturation/ enculturation. Mediation analyses investigate whether public stigma predicts 1) attitudes through self-stigma and 2) intentions toward help-seeking through self-stigma and attitudes. Moderation analyses investigate whether acculturation or enculturation moderate the serial mediation. Results. The relationship between public stigma and attitudes toward help seeking was mediated through self-stigma. Public stigma was positively associated with self-stigma, self-stigma was negatively associated with attitudes toward help-seeking, and attitudes were positively related to intentions to seek services. Acculturation and enculturation did not moderate the internalized stigma model. Discussion. Results support the applicability of the internalized stigma model among Canadian Muslims. Previous assertions that acculturation or enculturation influence the internalized stigma model are not supported.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.001 |
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".