Identifying the disruptions in the sexual response cycles of women with Sexual Interest/Arousal Disorder
Bibliographic record
Abstract
Various models have been conceptualized to explain human sexual response and sexual dysfunction. The present study used a circular model of sexual response, which distinguished between spontaneous and responsive desire, to investigate the location and number of breaks, defined as negative responses or the absence of positive responses, that occurred for women with low sexual desire. A total of 53 women who met diagnostic criteria for Sexual Interest/Arousal Disorder, and who were participating in a randomized trial of psychological treatment for low sexual desire participated (mean age=39.0 years). They were instructed to complete a sexual response cycle worksheet based on a recent sexual encounter. Conceptual content analysis was used to identify the number and location of breaks within the cycle. Women's written free responses to the different components of the sexual response cycle were also analyzed. Breaks were most often found with respect to the biological and psychological factors that impact sexual arousal. Many women also identified breaks in their sexual response cycles in the link between sexual arousal to responsive desire. Taken together, these findings provide support for the relevance and application of a circular sexual response cycle for women with low sexual desire that emphasizes the responsive nature of 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".