Unseen and Unheard: Exploring the Mental Health of Mostly Heterosexual College Students
Bibliographic record
Abstract
College years have long been understood to be a difficult yet important developmental period in an individual’s life, which may be particularly challenging for sexual minority students who tend to face discrimination on campus, which can undermine their mental health. Research in both college student and non-college student samples has shown that mostly heterosexual is a distinct sexual orientation. However, little is known about the wellbeing of individuals, including college students, who identify as mostly heterosexual. Moreover, among college students, little is known about the intersections between a mostly heterosexual identity and mental health. This study examined the association between sexual orientation and anxiety, depression, and risk for alcohol abuse. Specifically, it compared outcomes between students who identify as mostly heterosexual and students who identify as completely heterosexual. This study also compared outcomes between mostly heterosexual participants and lesbian, gay, bisexual, and queer (LGB+) students (as one group) to investigate potential differences among sexual minority students. In order to attempt to explain why differences exist, the mediating role of discrimination, namely incivility and hostility, were investigated. Several key findings emerged showing that mostly heterosexuals differ significantly from their completely heterosexual and LGB+ peers, in terms of their mental health and the role that forms of discrimination play in explaining disparities. Implications for the field of social work and other allied health professionals 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".