Seeking Care for Obsessive-Compulsive Symptoms Among African Americans: Findings From the National Survey of American Life
Bibliographic record
Abstract
• A small percentage (7.5%) of African Americans with OCD spoke to a medical professional about symptoms. • Few African Americans with obsessions (14.2%) or compulsions (7.6%) spoke to a medical professional. • Impairment from obsessions significantly increased the odds of seeking care. • Less education was associated with decreased odds of seeking care for obsessions. • Poorer self-rated mental health significantly increased the odds of seeking care. Although obsessive-compulsive disorder (OCD) is associated with clinically significant distress, many OCD patients do not seek treatment. Studies show that Black Americans with OCD are even less likely to obtain treatment due to differences in access. This study explored demographic and symptom outcomes associated with mental health service use for obsessions and compulsions among a nationally representative sample of African American adults ( n = 3,570). The analytic sample for this analysis is African Americans who endorsed either obsessions ( n = 435) or compulsions ( n = 543). Few respondents sought care from their doctor for obsessions (14.25%, n = 62) and even fewer sought care for compulsions (7.55%, n = 36). Respondents were significantly more likely to seek care for obsessions if they had poorer self-rated mental health and perceived impairment due to obsessions—however, they were significantly less likely to seek care for obsessions if they had a high school education or less. Additionally, respondents were more likely to seek care for compulsions if they had poorer self-rated mental health. Our findings suggest that demographic factors, such as level of education, can impact care-seeking behaviors and, therefore, treatment outcomes for African Americans with obsessive-compulsive symptoms. Knowledge of factors associated with OCD care-seeking behavior can help inform potential barriers to treatment and strategies to ensure equity in access to mental health care for this population. Clinical implications and future directions 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".