Sexuality myth endorsement is linked with poorer sexual and relational outcomes across demographic groups in a large and diverse sample
Bibliographic record
Abstract
Due to differences in access to sexual information, some people may be more likely to endorse sexuality myths, which could be linked to poorer sexual and relational outcomes. The goals of this study were to: 1) investigate predictors of sexuality myth endorsement; 2) examine links between endorsing myths and sexual and relational outcomes; and 3) assess whether group membership strengthened associations between myth endorsement and sexual and relational outcomes. A large and diverse sample of undergraduate students (N = 1,077) and community adults (N = 3,359) completed an online survey assessing their demographics, sexuality myth endorsement, and sexual and relational outcomes. Being assigned male at birth, identifying as cisgender, identifying as heterosexual, being younger, holding more conservative political views, being more religious, living in an urban locality during childhood, and not receiving sex education predicted greater sexuality myth endorsement. Greater sexuality myth endorsement predicted lower sexual satisfaction, lower sexual function (among people with vulvas), higher sexual distress, and lower relationship satisfaction. Generally, the strength of associations between myth endorsement and sexual and relational outcomes did not differ across demographic factors. Overall, findings provide insight into groups more likely to endorse sexuality myths, who thus may be at risk of poorer sexual and relational outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".