Coercive Sexual Experiences that Include Orgasm Predict Negative Psychological, Relationship, and Sexual Outcomes
Bibliographic record
Abstract
Psychological sexual coercion is known to negatively impact those who experience it, yet sexual encounters where orgasm is present are often presumed to be positive and absent of coercion. In the present study, we conducted an online survey with women ( n = 179) and men ( n = 251) to test associations between sexually coercive experiences that include orgasm and negative psychological, sexual, and relationship outcomes. To do so, we focused on three experiences: having an orgasm during coerced sex (CS), having a coerced orgasm during desired sex (CO), and having a coerced orgasm during coerced sex (COS). Using structural equation modeling, we found that ever having any of these coercion-plus-orgasm experiences with a current partner predicted significantly higher avoidance motivations (i.e., engaging in sex to avoid conflict with one’s partner), which in turn predicted significantly worse psychological distress, sexual satisfaction, relationship satisfaction, and sexual functioning (but not dyadic sexual desire). We also found that CS, CO, and COS predicted negative outcomes to a similar degree. However, testing gender/sex as a moderator clarified that CS predicted significantly lower sexual satisfaction, sexual functioning, and sexual desire for women but not men. Furthermore, CO predicted faking orgasms in women, but COS predicted faking orgasms in men. Together, results demonstrate that experiencing psychological sexual coercion and/or orgasm coercion is significantly associated with negative outcomes even if the coerced person’s orgasm occurs.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".