Within-person variability in relationship satisfaction moderates partners' pain estimation in vulvodynia couples
Bibliographic record
Abstract
Models of pain communication propose that the social environment contributes to partners' pain estimation. This study examined partners' pain estimation in vulvodynia, an idiopathic vulvovaginal pain condition that disrupts the sexuality and relationships of affected couples. Specifically, we investigated (1) the overall bias and tracking accuracy of male partners' perceptions of women's pain during intercourse and (2) the influence of men's within-person variability in relationship satisfaction on bias and accuracy. Sixty-nine women (mean age = 28.1, SD = 6.7) diagnosed with vulvodynia and their cohabiting male partners (mean age = 29.7, SD = 8.1) participated in an 8-week daily diary study. On sexual intercourse days (mean = 6.1, SD = 5.4), men reported their perception of women's pain during intercourse and women self-reported their pain. Men reported their daily relationship satisfaction on all diary days. Men's within-person variability in relationship satisfaction was represented by the SD of relationship satisfaction scores across all daily diaries. Results indicated that men's perceptions were both accurate in that they tracked changes in women's pain and biased in that they generally underestimated this pain. Men's variability in relationship satisfaction moderated tracking accuracy such that men with higher variability manifested lower tracking accuracy for women's pain. Men's higher variability in relationship satisfaction may interfere with their motivation to accurately infer their female partner's pain. Poorer pain estimation may impair men's ability to adjust their emotional and behavioral responses to women's pain, which may have negative consequences for the couples' coping with vulvodynia.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".