Web-Based Emotion Regulation Training for Sexual Health: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Effective emotional regulation (ER) skills are important for sexual function, as they impact emotional awareness and expression during sexual activity, and therefore, satisfaction and distress. Emotion regulation interventions may offer a promising approach to improve sexual health. Web-based emotion regulation may be a therapeutic strategy for men and women with sexual health concerns. Nevertheless, there is a scarcity of intervention trials investigating its effects in this context, much less using the internet. OBJECTIVE: This study aims to investigate the effects of a web-based emotion regulation training program for sexual function in both men and women. METHODS: The participants were recruited based on their self-reported sexual problems, which for men was defined by a score of <25 on the International Index Erectile Function (IIEF) and for women by a score of <26.55 on the Female Sexual Function Index (FSFI). The final sample included 60 participants who were randomized to either a web-based emotion regulation training for sexual function or to a waitlist control group. The treatment consisted of an 8-week web-based emotion regulation training for sexual function. The participants were assessed at baseline, post intervention, and the 3-month follow-up. RESULTS: Of the 60 participants included, only 6 completed all 3 assessment points (n=5, 20% in the treatment group and n=1, 5% in the waitlist control group) after receiving the intervention. At follow-up, there were no significant differences between groups in any measure. Among the intervention completers, large-to-moderate within-group effect sizes were observed between the assessment points on measures of emotion regulation, depression, lubrication, orgasm, thoughts of sexual failure, and abuse during sexual activity. The adherence rate was very low, limiting the generalizability of the findings. CONCLUSIONS: Participants who completed the intervention showed improvements in both sexual function domains and emotion regulation. Nonetheless, due to a high dropout rate, this trial failed to collect sufficient data to allow for any conclusions to be drawn on treatment effects. TRIAL REGISTRATION: ClinicalTrials.gov NCT04792177; https://clinicaltrials.gov/study/NCT04792177.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".