Orgasm consistency and its relationship to women’s self-reported and genital sexual response
Bibliographic record
Abstract
According to the Incentive Motivation Model (IMM) of sexual response, the rewarding and pleasurable aspects of a sexual act strengthen its incentive value and capacity to trigger sexual motivation. One such sexual reward is orgasm consistency, the percentage of time that orgasm is experienced during a sex act. Orgasm consistency may serve to influence the incentive value of a sexual behaviour. We tested this tenet of the IMM by examining whether orgasm consistency predicted women’s sexual responses to films depicting various sex acts. Data were collected from four separate studies examining women’s genital and subjective sexual response. Participants ( N = 144, age range = 18–65) were presented with neutral and erotic film stimuli while their genital arousal was assessed using vaginal photoplethysmography or thermography. Participants reported their sexual arousal level before, during, and after each stimulus presentation, and completed questionnaires assessing sexual history and experiences, sexual interests, and sexual functioning. Orgasm consistency during penile–vaginal intercourse (PVI) significantly predicted genital arousal to films depicting PVI, but similar relationships were not observed between genital or self-reported arousal and orgasm consistency during receptive oral sex and masturbation. Findings suggest that increasing orgasm consistency to a sex act may increase its incentive value, thereby triggering greater genital response to depictions of that act. Lack of consistent orgasm or generally pleasurable and rewarding sex may limit the capacity of sex acts to trigger sexual motivation in future sexual encounters, thus contributing to low sexual arousal and desire in 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".