Is Sexual Aversion a Distinct Disorder or a Trans-Diagnostic Symptom across Sexual Dysfunctions? A Latent Class Analysis
Bibliographic record
Abstract
Sexual aversion disorder (SAD) is a chronic condition that impacts sexual and psychological well-being. However, the relevance of SAD as a discrete disorder remains highly debated. This study aimed to clarify the status of SAD as either a distinct disorder or a trans-diagnostic symptom shared among sexual dysfunctions. This cross-sectional study used a latent class analysis approach among a Canadian community sample (n = 1,363) to identify how patterns of SAD symptoms (i.e., sexual fear, disgust, and avoidance) emerge across different spheres of sexual functioning (i.e., desire and arousal, erection or lubrication, genito-pelvic pain, and orgasm) and examine sociodemographic and psychosexual correlates of the identified classes. Examination of fit indices suggested four classes: Sexually functional, Impaired desire and responsiveness, Sexual aversion, and Comorbid sexual dysfunctions. Sexual aversion class members were more likely to be single, had experienced sexual assault in adulthood, and report lower levels of sexual satisfaction and psychological well-being, compared to Sexually functional class members. Results suggest that SAD is a distinct clinical syndrome, while its symptoms may co-occur with other sexual dysfunctions. To ensure that the needs of people with SAD are met with tailored treatment options, future nosography might consider reclassifying SAD as a specific disorder.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".