Who seeks sex therapy? Sexual dysfunction prevalence and correlates, and help-seeking among clinical and community samples
Bibliographic record
Abstract
Sexual dysfunctions (SD; e.g., female sexual interest/arousal disorder, erectile disorder, female orgasmic disorder, delayed ejaculation, genito-pelvic pain/penetration disorder, etc.) affect up to a third of individuals, impairing sexuality, intimate relationships, and mental health. This study aimed to compare the prevalence of SDs and their sexual, relational, and psychological correlates between a sample of adults consulting in sex therapy (n = 963) and a community-based sample (n = 1,891), as well as examine barriers to sexual health services for SD and the characteristics of individuals seeking such services. Participants completed an online survey. Analyses showed that participants in the clinical sample reported lower levels of sexual functioning and sexual satisfaction and higher levels of psychological distress than participants in the community-based sample. Moreover, higher SD rates were related to lower relational satisfaction and higher psychological distress in the community sample, and to lower sexual satisfaction in both samples. Among participants in the community sample who sought professional services for SD, 39.6% reported that they were unable to access services, and 58.7% reported at least one barrier to receiving help. This study provides important data regarding the prevalence of SD and the link between SD and psychosexual health in clinical and nonclinical samples, as well as barriers to treatment access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".