Understanding the impact of Mayer-Rokitansky-Küster-Hauser syndrome on sexual wellbeing—a qualitative study
Bibliographic record
Abstract
BACKGROUND: Mayer-Rokitansky-Küster-Hauser syndrome (MRKH) is characterized by the absence or underdevelopment of the uterus, cervix, and/or upper vagina. Individuals with MRKH often experience significant psychosexual impacts, including reduced self-esteem and sexual wellbeing. Yet, little is known about their lived experiences of sexual wellbeing. AIM: To explore sexual wellbeing among individuals with MRKH. METHODS: Participants (n = 12) were recruited through MRKH Canada and partook in semi-structured interviews centered on their experiences of navigating sexuality. Transcripts were subjected to a thematic analysis by a team of five coders. Themes and subthemes were synthesized and organized according to an inductively developed coding scheme. OUTCOMES: Themes and subthemes exploring sexual wellbeing of individuals with MRKH. RESULTS: Three themes captured the experiences of sexual wellbeing in individuals with MRKH. Navigating Sexual Norms touches on the influential role of knowledge and confidence in one's sexual life, the process of resisting norms for partnered sex, and coping with stigma and objectification. Navigating Sexual Relationships addresses evolving partner preferences, sexual communication, and how participants reconcile relationship insecurity. Navigating Genital Self-Image delves into the intricacies of struggles with genital image, coping strategies for genital insecurity, and how medical interventions impact genital image. CLINICAL IMPLICATIONS: Healthcare providers should address both medical and psychosocial aspects of sexual wellbeing in MRKH. Counseling should include communication strategies, sexual script flexibility, fertility-related concerns, and clear discussion of medical interventions and their impact on sexual wellbeing. Ultimately, comprehensive, ongoing support from both sexual partners and clinicians is essential as individuals with MRKH navigate their sexual lives. STRENGTHS AND LIMITATIONS: This study provides insights into the multifaceted experiences of sexual wellbeing in individuals with MRKH. The study is limited by a small sample size, which may not fully capture diverse experiences. CONCLUSION: Individuals with MRKH experience unique sexual challenges and demonstrate adaptation and resilience in the way that they navigate sexual norms, sexual relationships, and genital self-image.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".