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Record W4324352727 · doi:10.21203/rs.3.rs-2631536/v1

Health professionals’ perspectives on female genital cosmetic surgery: An interview study

2023· preprint· en· W4324352727 on OpenAlexaff
Maggie Kirkman, Amy Shields Dobson, Karalyn McDonald, Amy Webster, Pramasari Wijaya, Jane Fisher

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsWomen's Health Research Institute
FundersAustralian Research Council
KeywordsDiversity (politics)Construct (python library)Female circumcisionPsychologyHealth professionalsVulvaMedicineHealth careGynecologySociologySurgeryPolitical science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.367
GPT teacher head0.540
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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