Bisexual Stigma, Sexual Violence, and Sexual Health Among Bisexual and Other Plurisexual Women: A Cross-Sectional Survey Study
Bibliographic record
Abstract
Bisexual women experience higher rates of sexual victimization relative to heterosexual and lesbian women, and worse sexual health outcomes. Though these health disparities are well documented in the literature, few empirical data have been published on what factors are driving these disparities. Further, research documenting sexual victimization and health of plurisexual (i.e., attracted to more than one gender) women group all participants as bisexual. We do not know whether these experiences are similar across subgroups of plurisexual women. The current study reports on data from a cross-sectional survey, analyzing the relationships between bisexual-specific stigma and sexual violence, as well as other sexual health outcomes, across a sexually diverse group of plurisexual participants. Findings indicate that bisexual stigma is a significant predictor of lifetime sexual violence (odds ratio [OR] = 1.99 , p = .015) and verbal coercion (OR = 2.60, p = .004), but not other outcomes. There are differences across sexual identity categories, with bisexual participants being less likely to report sexual violence and verbal coercion, and less likely to access sexually transmitted infection/human immunodeficiency syndrome testing, compared to other plurisexual groups. Our findings support that bisexual stigma is an important factor to consider in understanding sexual violence disparities experienced by bisexual and other plurisexual women.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".