(007) THE ASSOCIATION BETWEEN MALE PARTNER SEXUAL DYSFUNCTION AND FEMALE SEXUAL DYSFUNCTION AMONG PREGNANCY PLANNING COUPLES
Bibliographic record
Abstract
Abstract Introduction Recent research has demonstrated an association between female sexual dysfunction and slower time to pregnancy. It remains unknown, however, to what extent male partner sexual function issues influence female sexual function in the preconception period. Objective To describe female partner perception of male sexual function issues in a population of pregnancy planners, and characterize the relationship between male sexual function issues and female sexual dysfunction. Methods We used cross-sectional data from Pregnancy Study Online (PRESTO), an online preconception cohort study of pregnancy planners residing in the United States or Canada. Primary participants (those with a uterus) completed a supplementary questionnaire about sexual function which included validated questionnaires and the following question about their partner’s sexual function: “In the past 4 weeks, has your partner experienced any issues that impacted your sex life?” Participants checked all that applied from a list of options (e.g., erectile dysfunction, mental health issues). Female sexual dysfunction and distress were categorized as binary variables using established cut points from the 6-item Female Sexual Function Index and Female Sexual Distress Scale. We descriptively report the prevalence of female-reported male partner sexual function issues in the sample and compare the prevalence of female sexual dysfunction and distress among couples with and without female-reported male partner issues. In a subset of couples in which the partner enrolled and provided data themselves, we compare the concordance of female-reported vs male-reported male sexual dysfunction. Results In our sample of 3577 female participants, 18% reported their male partner experienced sexual dysfunction in the past 4 weeks. Females who reported their partner had sexual dysfunction were more likely to meet the criteria for female sexual dysfunction (23% vs 17%) and sexual distress (34% vs 16%) than those who reported no partner issue. Among those reporting a male partner issue, the most common were low libido (40%), mental health issues (34%) and erectile dysfunction (26%). In a subset of couples with data from male partners (N = 933), 3.5% of partners reported erectile issues while 5.1% of female partners reported male erectile issues. In couples in which the female reported erectile dysfunction for their partner, only 14% of the partners also reported erectile dysfunction. When the male partner reported sexual dysfunction, 23% of female reported partner erectile dysfunction. Conclusions Female-perceived male sexual dysfunction was prevalent in this cohort of pregnancy planners. The most prevalent issues reported by female participants were low libido and poor mental health in their male partners. Male sexual dysfunction was associated with female sexual dysfunction. There was high discordance between couples regarding male sexual function. Previous research suggests that males underreport sexual dysfunction; thus, male sexual dysfunction might be an important, but overlooked, component of lowered fecundability. Incorporating sexual function data from both partners in clinical and research settings may improve the validity of self-report assessments. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: AbbVie, Inc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".