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Record W4382542110 · doi:10.1093/jsxmed/qdad068.028

(029) Prevalence of Sexual Pain Among Cis-Gender Muslim Women in North America

2023· article· en· W4382542110 on OpenAlexaboutno aff
Syed Rahman, Nadiah Mohajir, Hassoum Ceesay, M Bdaiwi, Y Khayr, W Akthar

Bibliographic record

VenueThe Journal of Sexual Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
Fundersnot available
KeywordsSnowball samplingSexual dysfunctionOutreachPopulationMedicineReproductive healthReligiosityHealth careCross-sectional studyPsychiatryClinical psychologyFamily medicinePsychologyDemographyEnvironmental healthSocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract Introduction Muslim women represent an underserved and underrepresented minority in North America. It is well documented that unconscious bias impacts the quality of healthcare delivery in the U.S. Stigmas related to sexual health in the Islamic culture may detrimentally impact a Muslim woman's ability to seek care as well. The prevalence rates of lifetime sexual pain disorders among all women, in general, are poorly studied and rates vary from 6 to 40% depending on the study. Even less has been studied in this population with an isolated study from one clinic demonstrating about a 14% prevalence of sexual pain in Muslim women with sexual dysfunction. It is important to understand the burden of sexual pain and dysfunction among Muslim women to improve the quality of their care. Objectives The objective of this study was to investigate the prevalence of sexual violence, sexual dysfunction, and spiritual abuse among Muslims aged 18-45 and understanding the barriers to seek healthcare and assistance. Methods A national cross-sectional, IRB approved study was implemented to investigate the above. Through the use of the Qualtrics online platform, a 151- question survey was designed and implemented which included questions about demographics as well as questions about religiosity, sexual dysfunction, sexual pain, varying types of sexual abuse, and follow-up treatment. Participants were recruited using a snowball sampling approach via social media, listserv outreach, office visits, and mobilizing key community partners to help disseminate the survey. Descriptive statistics were utilized to better understand the prevalence and will further attempt to examine the correlation between barriers to seeking assistance and identity, attitudes, and experiences of sexual violence. Results From March 2020 until November 2020, a total of 704 Muslims ranged from 18-45 years old across the United States and Canada responded to the survey. Of those, 86% identified themselves as cis-gender women. Of those women, 47% chose to answer the questions regarding sexual dysfunction and sexual pain and almost half of the respondents (42%) admitted to having a history of sexual pain and dysfunction. Among those women who had a history of sexual pain, 65% admitted that they had never sought help for their pain from any healthcare provider. Conclusions Sexual pain and sexual dysfunction are under-reported among Muslim women and there are clear barriers that need to be overcome to alleviate their suffering. This is the first national study of its kind that has looked at sexual dysfunction and sexual pain, specifically, and is a great stepping stone to further analysis and investigation. At least half of the cis-gender women in this national survey admitted to some experience with sexual dysfunction, more specifically sexual pain, in their lifetime and more than two-thirds of those did not seek treatment. Cultural and religious stigmas, as well as possible barriers to seeking healthcare by lack of cultural competence training by many providers, may be contributing to their lack of follow up for treatment. With additional studies and analysis as well as building awareness, we can better understand how to eliminate barriers and stigmas and increase education for this population. Disclosure No.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.305
Teacher spread0.248 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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