Female sexual desire: what helps, what hinders, and what women want
Bibliographic record
Abstract
An Enhanced Critical Incident Technique (ECIT) was used to examine what helps, what hinders, and what might help female sexual desire. Nine women in cohabitating, long-term relationships were interviewed to explore their lived experiences of sexual desire. Each participant was asked what sexual desire means to them/how they define it, what helps and hinders their sexual desire, and what they think could help their sexual desire. ECIT analysis of participant responses resulted in the identification of 246 critical incidents, 114 helping incidents, 98 hindering incidents, and 34 wish list items, which fit into a scheme of 12 categories. Findings revealed that women’s sexual desire is a composite construct: there is a vast diversity and multidimensionality in the way sexual desire is defined and experienced. Factors that help/hinder/might help range from intrapersonal and relational factors to logistical, sociocultural, and systemic. The 12 categories can act as a framework for areas of clinical inquiry when treating concerns regarding female sexual desire. The multitude of helping and wish-list factors discovered emphasize the importance of positive-psychology and sex-positive approaches to female sexual desire. Counselling implications include widening the intrapersonal and relational focus to address and include sociocultural, economic, political, and other contextual concerns.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".