Psycho-Relational Well-Being in Women with Sexual Pain: A Preliminary Study
Bibliographic record
Abstract
Female sexual pain is a complex multi-factorial condition. Most of the research has focused on underlying biomedical factors. Although psychological and relational factors have been studied as possible correlates, data are still controversial. The aim of the present study was to investigate psychological and relational well-being in women who complain of sexual pain. The hypothesis was that sexual pain is associated with worse scores. 377 women, 131 with sexual pain (mean age 29.48±8.49) and 246 without sexual pain (mean age 29.23±8.11), recruited with snowball method. Women completed: a socio-demographic questionnaire, the McGill Pain Questionnaire (MPQ), the Symptom Checklist (SCL-90-R), the Beck Depression Inventory (BDI-II), the State-Trait Anxiety Inventory (STAI-Y), the Toronto Alexithymia Scale (TAS-20), the Dyadic Adjustment Scale (DAS) and the Short Form of Health Survey Questionnaire (SF-36). Factorial one-way MANOVAs were used to analyze differences between groups. The group with sexual pain totalized significant worse scores in: somatization (F(1,338)=7.827 p<.01), obsessive-compulsive (F(1,338)=6.377 p<.05), depression (F(1,388)=9.668 p<.01), hostility (F(1,388)=4.619 p<.05), paranoid ideation (F(1,388)=4.114 p<.05) subscales of SCL-90-R; depression (F(1,338)=16.113 p<.001) of BDI-II; anxiety state (F(1,338)=11.41 p<.01) and trait (F(1,338)=10.638 p<.01) of STAI-Y; alexithymia (F(1,370)=8.97 p<.01) of TAS-20; general health (F(1,356)=26.67 p<.001) of SF-36. No significant differences between groups were found in dyadic adjustment, showing that sexual pain could not have a negative impact on the quality of relationship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".