Cultural competency in the treatment of obsessive-compulsive disorder: practitioner guidelines
Bibliographic record
Abstract
Abstract This article provides clinical guidelines for basic knowledge and skills essential for successful work with clients who have obsessive-compulsive disorder (OCD) across ethnic, racial and religious differences. We emphasise multiculturalist and anti-racist approaches and the role of culture in shaping the presentation of OCD in clients. Several competencies are discussed to help clinicians differentiate between behaviour that is consistent with group norms versus behaviour that is excessive and psychopathological in nature. Symptom presentation, mental health literacy and explanatory models may differ across cultural groups. The article also highlights the possibility of violating client beliefs and values during cognitive behavioural therapy (CBT), and subsequently offers strategies to mitigate such problems, such as consulting community members, clergy, religious scholars and other authoritative sources. Finally, there is a discussion of how clinicians can help clients from diverse populations overcome a variety of obstacles and challenges faced in the therapeutic context, including stigma and cultural mistrust. Key learning aims (1) To gain knowledge needed for working with clients with OCD across race, ethnicity and culture. (2) To understand how race, ethnicity and culture affect the assessment and treatment of OCD. (3) To increase awareness of critical skills needed to implement CBT effectively for OCD in ethnoracially diverse clients. (4) To acknowledge potential barriers experienced by minoritized clients and assist in creating accessible spaces for services.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".