Orgasm Consistency in Mixed-Gender Couples: Actor, Partner, and Discrepancy Effects from Dyadic Response Surface Analysis
Bibliographic record
Abstract
The potential link between orgasm consistency (i.e., the percentage of time an individual experiences orgasm during sexual interactions with a partner) and sexual satisfaction in mixed-gender sexual relationships remains underexamined in the literature. We combined two dyadic samples (N = 725 couples) and utilized Dyadic Response Surface Analysis (DRSA) to examine how both partners' orgasm consistency and their discrepancy of orgasm consistency predict both partners' sexual satisfaction. We found that partners' discrepancy in orgasm consistency was not uniquely connected to higher sexual satisfaction for either women or men; rather, the overall consistency of orgasm was connected to better sexual satisfaction for both partners. In addition, there was some evidence tentatively suggesting that men were more likely than women to report lower sexual satisfaction if his partner was orgasming more consistently than he was, as opposed to her reporting lower sexual satisfaction from him orgasming more consistently than she was; though this appears to be a rare scenario as only 5.9% of couples had women who orgasmed more consistently than men. This study may assist educators and clinicians as they help couples consider the sexual scripts surrounding orgasm consistency, and how they can attend to each others' desires in a way that maximizes sexual satisfaction for both partners.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".