Obsessive-compulsive symptoms and related risk and protective factors in Black individuals in Canada
Bibliographic record
Abstract
Background: Data from the United States showed that Black individuals face unique issues related to obsessive-compulsive disorder (OCD). However, Canadian research on OCD among Black individuals remains very limited. The present study aims to document obsessive-compulsive (OC) symptoms and related risk and protective factors in Black individuals aged 15 to 40 years old in Canada. Methods: A total of 860 Black individuals (75.6% female) aged 15-40 years were recruited as part of the Black Community Mental Health in Canada (BcoMHealth) project. Independent t-tests, ANOVA, and multivariable linear regressions were used to assess OC symptom severity and identify risk and protective factors. Results: Black individuals presented high levels of OC symptoms. Results showed that Black individuals born in Canada experienced more OC symptoms compared to those born abroad. Results also showed that there were no differences between Black women, Black men, and those who identified their sex as "other." Everyday discrimination, internalized racism, and microaggressions positively predicted OC symptoms, while social support negatively predicted OC symptoms. Limitations: Limitations of this study include its cross-sectional nature, which prevents us from establishing causal links, not assessing for the clinical diagnosis of OCD, and using self-report measures. Results support that different forms of racial discrimination contribute to the development and severity of OC symptoms in Black individuals in Canada. Social support may play a protective role for those individuals. These factors must be considered in future research and in the assessment and treatment of Black individuals with OCD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".