Group Psychotherapy in Specialty Clinics for Substance Use Disorder Treatment: The Challenge of Ethnoracially Diverse Clients
Bibliographic record
Abstract
Minimal research has explored how clinicians address race and ethnicity considerations in the context of group psychotherapy within substance use disorder (SUD) specialty treatment settings. This article is an exploratory qualitative study in an effort to narrow this gap, using data from semi-structured interviews with 13 group clinicians at three outpatient SUD specialty clinics in the United States. Results are drawn from the portion of coded material pertaining to ethnoracial considerations. A predominant theme from the interviews was the importance of individualized care in terms of "meeting clients where they are at." However, minimal attention appears to have been given to addressing clients' demographic diversity. Overall, ethnoracial considerations were minimally addressed in groups, with clinicians framing such primarily in terms of "cultural" factors relevant to clinics' treatment philosophies. Moreover, limited attention was reportedly given to acknowledgment of social inequities faced by ethnoracial minority clients (e.g., racial discrimination), even though a few clinicians reported concern that minority clients were less engaged in treatment. Clinical implications of these findings and recommendations for future research are discussed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".