Mental health disparities among sexual and gender minority students in higher education
Bibliographic record
Abstract
Objective: There has been an ongoing mental health crisis among sexual and gender minority (SGM) populations. This continues amidst rising population-level depression and suicide rates, especially among students in higher education. This work aims to understand changes in SGM student mental health over time. Participants: N = 483,574 responses to the Healthy Minds Study (2007C2022) were examined from 18 to 35-year-old U.S. college and university students. Methods: Linear and logistic regressions were used to examine associations between SGM identity and mental health. Mediation and structural equation modeling were used to examine potential links among risk factors. Results: On average, ∼18% of students identified as SGM, which included a 6-fold increase in SGM self-identification across this 15-year period. Depression rates increased over time; ∼12% of students reported major depression. SGM students were 3.18 times (z = 111.16, p < .001) more likely to report depression than non-SGM students (26.85% vs. 8.53%). Disproportionate discrimination and lack of school belonging partially explained SGM disparities in depression. SGM students were twice as likely to utilize therapy (z = 115.42, p < .001) but half as likely seek help from family (z = 55.48, p ≤ .001). Conclusions: Academic institutions must take concrete steps to reduce barriers mental health care, combat discrimination, and bolster community belonging and interpersonal support for SGM students.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".