Health professionals’ perspectives on female genital cosmetic surgery: An interview study
Bibliographic record
Abstract
Abstract Background Female genital cosmetic surgery (FGCS) changes the structure and appearance of healthy external genitalia. We aimed to identify discourses that help explain and rationalise FGCS and to derive from them possibilities for informing clinical education. Methods We interviewed 16 health professionals and 5 non-health professionals who deal with women’s bodies using a study-specific semi-structured interview guide. We analysed transcripts using a three-step iterative process: identifying themes relevant to indications for FGCS, identifying the discourses within which they were positioned, and categorising and theorising discourses. Results We identified discourses that we categorised within four themes: Diversity and the Normal Vulva (diversity was both acknowledged and rejected); Indications for FGCS (Functional, Psychological, Appearance); Ethical Perspectives; and Reasons Women Seek FGCS (Pubic Depilation, Media Representation, Pornography, Advertising Regulations, Social Pressure, Genital Unfamiliarity). Conclusions Vulvar aesthetics are a social construct to which medical practice and opinion contribute and by which they are influenced; education and reform need to occur on all fronts. Resources that not only establish genital diversity but also challenge limited vulvar aesthetics could be developed in consultation with women, healthcare practitioners, mental health specialists, and others with knowledge of social constructs of women’ bodies.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".