Using network analysis to model associations between psychological symptoms, sexual function, and sexual distress in women
Bibliographic record
Abstract
Background: Psychological difficulties, including depression, anxiety, and somatization, are among the most important predictors for women's sexual function (i.e., arousal, desire, lubrication, pain, and satisfaction) and sexual distress. These associations have largely been studied at the construct level, with little research examining which specific symptoms might be most important for maintaining links between psychological difficulties and domains of sexual function. The present research sought to establish and characterize networks of women's psychological symptoms, sexual function, and sexual distress, and identify potential bridge symptoms that connect them. Methods: In a cross-sectional study, 725 women reported on their sexual function, sexual distress, and depressive, anxiety, and somatization symptoms. A series of network analyses was used to identify central symptoms and connections between psychological symptoms, sexual function domains, and sexual distress. Results: Across the modeled networks, sexual distress and pain during sex were consistent bridges between other sexual function domains and psychological symptoms. Discussion: Overall, our models revealed sexual distress as an important potential mediator between sexual function problems and psychological symptoms that might contribute to the development and maintenance of comorbid sexual function and psychological problems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".