Perceived reproductive health needs among Muslim women in the southern US
Bibliographic record
Abstract
Despite growing rates of Muslims in the United States, we know little about the health of Muslim women in this country. Due to the stigma surrounding sex, sexuality, and the cultural beliefs of this population, there may be unique and unknown challenges regarding access to reproductive health care for Muslims in the US. Purpose: This study aims to examine variables that promote and impede access to reproductive health care for Muslim women in the southern US. Methods: This multi-method study included in-person semi-structured interviews (n=15) and an anonymous online survey (n=76). Findings: Participants generally had low rates of gynecological care and cervical cancer screening. The cultural (e.g. waiting for marriage to receive gynecological care) and contextual aspects (e.g. gender) that increased or restricted access to care in terms of screening, providers and education are discussed. We also identified some misconceptions related to screening and contraception. Conclusions: Influences on reproductive health care experienced by participants in this study have similarities yet are distinct from Muslim populations in other countries as well as other groups of women in the US. This study points to a need for more population-focused education of providers, as well as awareness about reproductive health and health care recommendations and access for Muslim women.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".