Intersecting Identities and Campus Well-Being: Mental Health, Substance Use, and Service Barriers Among Racial/Ethnic and Sexual/Gender Minoritized Students at a Canadian University
Bibliographic record
Abstract
Sexual and gender minoritized (SGM) and racial and ethnic minoritized (REM) and university students face unique minority stressors that increase risk of mental health problems and substance use. Yet, little research examines how intersecting identities influence barriers to accessing campus services. We investigated associations between these identities - including their intersections - and mental health, substance use, and obstacles to utilizing campus-based services. We conducted an online survey with 1,009 undergraduate students (Mage= 20.6, SD= 5.1) at a Canadian university about mental health symptoms and substance use, as well as experiences with and barriers to accessing campus mental health and substance use services. Regression analyses revealed few differences in mental health severity and barriers to accessing campus services between most REM subgroups and white students. Most REM subgroups reported lower rates of substance use, greater awareness of campus services, and found services more helpful relative to white students. Sexually minoritized students reported greater severity of mental health symptoms and more barriers to accessing campus services relative to heterosexual students. Interactions between SGM and REM identities did not correspond with differing outcomes. Our findings highlight the need for tailored campus services that address unique challenges faced by diverse minoritized student populations.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".