An exploration of the prevalence of global, categorical, and specific female genital dissatisfaction
Bibliographic record
Abstract
Genital dissatisfaction is problematic for women in and of itself but also because it is associated with poorer sexual well-being. The current study aimed to clarify the prevalence of female genital dissatisfaction, both globally (i.e., overall) and with regards to distinct genital aspects, in a sample of women of different ages and with different relationship statuses. Participants were 209 women (ages 20 to 68 years) living primarily in the United States. Participants completed an online survey that included a background questionnaire, the 7-item Female Genital Self-Image Scale, and the 30-item Specific Genital Aspects Scale. Overall, 18% (n=37) of the women were globally dissatisfied with their genitals. Between 11% and 20% (n=22−41) of the women were dissatisfied with each categorical genital aspect (i.e., appearance, smell/taste, and function). The women were significantly less likely to be dissatisfied with their genital function than with their genital appearance. Between 2% and 69% (n=4−145) of the women were dissatisfied with each of the 30 genital aspects at the specific level. More than one quarter of the women were dissatisfied with nine (of 30) specific genital aspects and these spanned all three categories of genital self-perceptions. There were no differences in the prevalence of global or categorical genital dissatisfaction across age or relationship status. The results are discussed in terms of their implications for educators, researchers, clinicians, and journalists.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".