Mental health service use among Black adolescents in Ontario by sex and distress level: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Race is a social construct reflecting broader systemic forces that can affect health, including mental health. We sought to ascertain whether patterns of mental health care service use are associated with race among adolescents in Ontario. METHODS: We conducted a cross-sectional study using data from the 2015-2019 Ontario Student Drug Use and Health Survey. We assessed mental health care access for students in grades 7-12 younger than 20 years based on their responses about their care usage in the past 12 months. We used logistic and Poisson regression models to analyze differences in service utilization, with interaction terms for sex and mental distress (measured using the Kessler Psychological Distress Scale-6 Items). RESULTS: Black male students with low distress were nearly twice as likely as White males to report initiating care (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.09-2.06). However, when Black males' distress worsened to moderate levels, they became less than half as likely to access care than their White peers (OR 0.41, 95% CI 0.20-0.84). Black females faced disparities at all distress levels, with the gap widening as distress increased (moderate distress OR 0.78, 95% CI 0.46-1.34; serious distress OR 0.60, 95% CI 0.40-0.89). Even after initiating care, Black females mostly had lower odds of access frequency than White females (low distress OR 0.78, 95% CI 0.66-0.92; moderate distress OR 1.00, 95% CI 0.84-1.19; serious distress OR 0.60, 95% CI 0.42-0.85). INTERPRETATION: Black survey respondents with psychological distress were less likely to report using mental health services than their White peers, with Black female respondents being the least likely to access care. Policy and practice should seek to address systemic racism and a lack of culturally relevant care for Black adolescents with mental distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".