A FEASIBILITY AND USABILITY PILOT STUDY TESTING A SEX EDUCATION AND THERAPY ONLINE INTERVENTION FOR THE TREATMENT OF SEXUAL DESIRE DISCREPANCY IN COUPLES
Bibliographic record
Abstract
Abstract Objectives This study addressed one of the European Society for Sexual Medicine’s priorities by developing the first research-informed and evidence-based dyadic treatment to guide healthcare professionals in managing sexual desire discrepancy (SDD) in couples. According to expert opinion, treatment should incorporate psychoeducation and sex therapy, such as Supportive Sex Education and Therapy (STEP), a newly created intervention with strong and lasting evidence of efficacy for women with Sexual Interest/Arousal Disorder (SIAD). The goal of the present study was to adapt this intervention to couples with SDD and assess feasibility (acceptability, demand, practicality, and limited clinical efficacy) and usability when delivered in an online format. Methods The eight-session treatment was delivered weekly to 20 couples who completed feasibility measures at baseline and immediately posttreatment. Immediately before each session, couples completed online usability measures regarding the previous session. Primary outcomes included acceptability, demand, practicality, and usability, while secondary outcomes focused on limited clinical efficacy. Results Primary outcomes showed couples reported high overall satisfaction with STEP, stating it significantly met their expectations. Home exercises were well-aligned with therapeutic goals and helped manage their difficulties. After treatment, couples experienced a more natural sense of shared sexual desire compared to their earlier discrepancy and felt more confident in engaging in sexual activity. Most couples were satisfied with the treatment’s speed and duration, finding it moderately easy to use. They also reported a moderate intention to continue the program in the future. Most participants completed all exercises, despite moderate challenges and external obstacles. Couples understood the instructions well, including the rationale behind each exercise, and appreciated the specificity of the guidelines, feeling highly involved in exercise planning. Secondary outcomes showed significant improvements in clinical efficacy. Conclusions This pilot study suggests that STEP is feasible and usable, providing the foundation for a larger randomized-controlled trial with a larger sample and longer follow-up period. Conflicts of Interest No conflicts of interest to declare.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".