Predictors of Psychological Distress for Bi + Individuals during the COVID-19 Pandemic
Bibliographic record
Abstract
Bisexual and pansexual (bi+) individuals remain understudied subpopulations of the lesbian, gay, bisexual, transgender and queer (LGTBQ+) community, despite experiencing complex stressors and stigma by both LGBTQ + and heterosexual communities. In this study, we used correlation analyses, hierarchical linear regression and moderation analyses to explore the relationships between societal stressors (concern about COVID and discrimination) and a protective factor (social support) in a sample bi + undergraduate and graduate students. The correlation results revealed positive, bivariate relationship between worry about COVID and psychological distress, along with everyday discrimination and psychological distress. The correlation results also revealed a negative bivariate correlation between social support and psychological distress for bi + participants. Hierarchical linear modeling revealed that worry about COVID, everyday discrimination and social support were significant predictors of psychological distress for bi + individuals. Finally, moderation analyses revealed that social support moderated the relationship between everyday discrimination and psychological distress but not the relationship between COVID-Worry and Psychological Distress for bi + individuals. Findings from the present study will help inform clinicians of specific risks to bi + individual’s mental health when working with this population, specifically regarding the impact of the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".