From Sexual Desire Discrepancies to Desirable Sex: Creating the Optimal Connection
Bibliographic record
Abstract
Beginning in 2005, our team conducted a series of studies on optimal sexual experiences. We have applied our findings to develop a group therapy intervention for couples presenting with low sexual desire/frequency and sexual desire discrepancy. The goal was to improve the quality of erotic intimacy by focusing on such elements as being fully embodied during sex, increasing authenticity, trustworthiness, and vulnerability. Twenty-eight heterosexual individuals (14 couples) were seen in 16 hours of couples group therapy. Each completed the New Sexual Satisfaction Scale in pretests, posttests and six-month follow-ups. Significant differences in satisfaction (p <.001) were found in overall sample means from pretests to posttests and follow-ups. Significant differences were also found in 10 of 20 items, plus in two of three added items, including satisfaction with intensity of sexual arousal, variety, frequency, partner's initiation, and emotional opening up. Although this is a small sample, the results indicate that this intervention is effective. We interpret these findings in terms of creating just enough safety to enable couples to take erotic risks and thereby create desirable sexual intimacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".