Female sexual desire: what helps, what hinders, and what women want
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it