Remote Versus In-Person Group Therapy for Couples Distressed by Low/No Sexual Desire/Frequency or Sexual Desire Discrepancy: A Response to COVID-19
Bibliographic record
Abstract
Sexual desire problems are among the most common in sex therapists’ offices and considered among the most difficult to treat. Prior to COVID-19, we had developed an 8-week, 16-h group couples sex therapy approach to help couples distressed by sexual desire discrepancy, by targeting the couple rather than the identified patient with low desire. Because of the pandemic, we tailored our approach to offer it remotely. To our knowledge, this is the first investigation of a group therapy for couples via teletherapy. In total, 141 couples completed the therapy, 75 prior to COVID-19 and 66 during, including 23 sexual, gender and relationship minority couples. Participants completed the New Sexual Satisfaction Scale at four time-points and provided written feedback. Results suggested that this therapy is effective in person and online at post-test (p < .001) and at 6-month follow-up (p < .001). Participants described positive changes in authenticity, vulnerability, trust, playfulness, embodiment and empathic communication, especially during times of conflict. This brief, affordable and accessible intervention can be used to deal with sexual desire problems. This approach enhances sexual fulfillment and enables clients to revision sexuality itself, thereby freeing them to create sex worth wanting and enduring desire.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".