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Record W4376272470 · doi:10.1093/jsxmed/qdad063

Decision making in metoidioplasty and phalloplasty gender-affirming surgery: a mixed methods study

2023· article· en· W4376272470 on OpenAlexaboutno aff
Rebecca L. Butcher, Linda Kinney, Gaines Blasdel, Glyn Elwyn, Jeremy B. Myers, Benjamin Boh, Kaylee M. Luck, Rachel A. Moses

Bibliographic record

VenueThe Journal of Sexual Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsPhalloplastyTransgenderMedicineSex reassignment surgery (male-to-female)Gender dysphoriaSurgeryPsychologyPenisTranssexualPsychoanalysis

Abstract

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BACKGROUND: Gender-affirming surgical procedures, such as metoidioplasty and phalloplasty for those assigned female at birth, are complex and multistaged and involve risks. Individuals considering these procedures experience greater uncertainty or decisional conflict, compounded by difficulty finding trustworthy information. AIM: (1) To explore the factors contributing to decisional uncertainty and the needs of individuals considering metoidioplasty and phalloplasty gender-affirming surgery (MaPGAS) and (2) to inform development of a patient-centered decision aid. METHODS: This cross-sectional study was based on mixed methods. Adult transgender men and nonbinary individuals assigned female at birth at various stages of MaPGAS decision making were recruited from 2 study sites in the United States to participate in semistructured interviews and an online gender health survey, which included measures of gender congruence, decisional conflict, urinary health, and quality of life. Trained qualitative researchers conducted all interviews with questions to explore constructs from the Ottawa decision support framework. OUTCOMES: Outcomes included goals and priorities for MaPGAS, expectations, knowledge, and decisional needs, as well as variations in decisional conflict by surgical preference, surgical status, and sociodemographic variables. RESULTS: We interviewed 26 participants and collected survey data from 39 (24 interviewees, 92%) at various stages of MaPGAS decision making. In surveys and interviews, affirmation of gender identity, standing to urinate, sensation, and the ability to "pass" as male emerged as highly important factors for deciding to undergo MaPGAS. A third of survey respondents reported decisional conflict. Triangulation of data from all sources revealed that conflict emerged most when trying to balance the strong desire to resolve gender dysphoria through surgical transition against the risks and unknowns in urinary and sexual function, appearance, and preservation of sensation post-MaPGAS. Insurance coverage, age, access to surgeons, and health concerns further influenced surgery preferences and timing. CLINICAL IMPLICATIONS: The findings add to the understanding of decisional needs and priorities of those considering MaPGAS while revealing new complexities among knowledge, personal factors, and decisional uncertainty. STRENGTHS AND LIMITATIONS: This mixed methods study was codeveloped by members of the transgender and nonbinary community and yielded important guidance for providers and individuals considering MaPGAS. The results provide rich qualitative insights for MaPGAS decision making in US contexts. Limitations include low diversity and sample size; both are being addressed in work underway. CONCLUSIONS: This study increases understanding of the factors important to MaPGAS decision making, and results are being used to guide development of a patient-centered surgical decision aid and informed survey revision for national distribution.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.152
GPT teacher head0.503
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations20
Published2023
Admission routes1
Has abstractyes

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