Examination of perceived religion in Muslim women’s access to counseling and psychotherapy services: An audit study.
Bibliographic record
Abstract
Across the United States and Canada, the marginalization of Muslims has contributed to many Muslim women having mental health difficulties, making it essential that services are available and accessible. An email correspondence audit design research study was used to investigate whether mental health practitioners demonstrate implicit bias in the form of aversive prejudice against Muslim women during a request for counseling/psychotherapy services. A total of 450 counselors or psychologists participated. Practitioners received an email from either a Muslim or non-Muslim woman, signified by name and a religious quotation, requesting an appointment. Based on the Aversive Racism Framework, it was hypothesized that practitioners would (a) respond more frequently to the Muslim woman and (b) respond faster to the Muslim woman but (c) offer services to the Muslim woman at a lesser or similar frequency. All three hypotheses were supported. Findings suggest that aversive prejudice appears active at the forefront of counseling and psychotherapy services for Muslim women, whereby counselors and psychologists are unknowingly acting in a biased manner toward a request for an appointment from a Muslim woman. Suggestions for overcoming this bias are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".