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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".