Diagnosing and Treating Mental Illness Across Cultures: Systemic Racism in Clinical Psychology
Bibliographic record
Abstract
Diverse cultures have historically been underrepresented by psychological research (Arnett, 2008; Nielsen et al., 2017). Using western data and diagnostic criteria designed by western society leads to contemporary understandings of clinical diagnoses and psychotherapies that lack external validity beyond western society. Consequently, when immigrants from these diverse countries seek mental health services, they are disproportionately misdiagnosed and receive psychotherapies that are far less effective. The tools and training that clinicians are provided with do not effectively translate through different cultural lenses. Contemporary diagnostic instruments like the Diagnostic and Statistical Manual of Mental Disorders (DSM) need to include additional representative research to improve their sensitivity across cultures. Furthermore, psychotherapies need appropriate cultural adaptations that connect with cultural minority clients to become properly effective. Diagnostic manuals and empirically supported psychotherapies are culturally biased descriptions of clinical psychology which need cultural competence to accommodate the growing cultural diversity present within Canada and America. This literature overview details the extent of the underrepresentation in western psychological research. Subsequently, it presents a brief account of diverse cultural research demonstrating how mental health expression varies extensively by culture. Finally, expounding on these points demonstrates how the resulting DSM is not adequate for the countries that use it, and the resulting psychotherapies lack efficacy in their populations, perpetuating systemic racism in clinical psychology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".