(266) Examining the Experience of Sexual Dysfunction and Spiritual Abuse among Cisgender Muslim Women in North America
Bibliographic record
Abstract
Abstract Introduction Muslim women are an underrepresented and underserved population in North America who often do not seek care for their sexual traumas and sexual difficulties. In order to better serve this population with minimal bias, it is important for healthcare providers to understand their vast experiences with spiritual abuse and sexual dysfunction, as well as reasons for not seeking healthcare related to sexual dysfunction. Objective To understand the experiences of cisgender Muslim women in North America with regards to sexual function, sexual abuse and spiritual abuse. Methods To investigate these topics, a survey was administered to 771 Muslims between the ages of 18–45 who live in the US and Canada 2020. The majority of respondents were heterosexual females, were part of the Sunni sect of Islam, had a bachelor’s degree or higher, and were of South Asian, Asian, Middle Eastern, or Arab ancestry. Aspects of sexual functioning such as the frequency of sexual desire, arousal during sexual intercourse, achievement of orgasm, and pain during or after vaginal penetration were assessed. Results Of the 158 respondents who reported having experienced sexual pain, only 34.4% sought healthcare for this problem. The most commonly cited reasons included fear or shame, unawareness, doubt that the situation would improve, and resolution of the pain on its own. 530 participants were asked if religion or spirituality have been used to: force them to make decisions they are opposed to (76.4%), force them to engage in a non-consensual sexual act (14.6%), isolate from others (57.9%), minimize the abuse they experienced (48.7%), or prevent them from speaking out against abuse (53.9%). Conclusions This data provides insight into a Muslim cohort’s frequency of sexual burdens which include dysfunction and abuse. Understanding how spiritual abuse can influence patients’ familial, romantic, and sexual relationships may help providers to administer culturally competent care to Muslim patients. 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".