Genito-Pelvic Pain Through a Dyadic Lens: Moving Toward an Interpersonal Emotion Regulation Model of Women’s Sexual Dysfunction
Bibliographic record
Abstract
Researchers and clinicians alike widely acknowledge the inherently interpersonal nature of women's sexual dysfunctions given that both partners impact and are impacted by these difficulties. Yet theoretical models for understanding the role of interpersonal factors in women's sexual dysfunctions are severely lacking and have the potential to guide future research and inform more effective interventions. The most widely studied sexual dysfunction in women that has espoused a dyadic approach by including both members of affected couples is genito-pelvic pain/penetration disorder (GPPPD). In this article we use the example of GPPPD to introduce a novel interpersonal emotion regulation model of women's sexual dysfunction. We first review current knowledge regarding distal and proximal interpersonal factors in GPPPD. Then, we describe our theoretical model and consider relevant pain and sex-related research on emotion regulation processes-emotional awareness, expression, and experience-in the context of GPPPD, including sexual function, satisfaction, and distress. Next, we review how existing theories from the fields of chronic pain and sex and relationships research have informed our model and how our model further builds on them. Finally, we discuss the implications of our model and its applications, including to other sexual dysfunctions 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".