Exploring Attention-Deficit/Hyperactivity Disorder (ADHD) Symptomatology in Relation to Women’s Orgasmic Consistency
Bibliographic record
Abstract
This well-powered, pre-registered online study examined differences in orgasmic consistency among women both with and without ADHD symptomatology while controlling for sexual assertiveness and sexual attitudes, constructs yet to be explored in this context. A convenience sample of 815 (Mage = 28.93, SDage = 9.23) cisgender females, at least 18 years of age and sexually active with at least one partner over the last 6 months, completed this study online through the survey platform Qualtrics. No preexisting ADHD diagnosis was required. Study hypotheses were confirmed, revealing that ADHD symptomatology predicted orgasmic consistency, and most notably, that greater inattentive ADHD symptomatology was associated with lower consistency of orgasm. Exploration of medication use for ADHD symptom management revealed only a significant effect of medication use on orgasmic consistency in women who did not currently meet the criteria for ADHD symptomatology. Finally, when comparing women of sexual minority orientations to the sexual majority, results indicated only a significant difference in orgasmic consistency rates among women who did not meet the ADHD symptomatology criteria. Given that women who struggle with difficulties in consistently achieving orgasm are more likely to experience negative relationship satisfaction, self-esteem, and sexual satisfaction outcomes – as well as increased rates of emotional distress – our results have significant implications for the sexual health and well-being of women with ADHD symptomatology. This is particularly true for those with the inattentive ADHD symptomatology subtype.
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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 | 0.001 | 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 | 0.000 |
| 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".