Menopause symptoms, sexual dysfunctions and pelvic floor disorders in refugee and asylum seeker women: a scoping review
Bibliographic record
Abstract
Refugee and asylum seeker women face a variety of health challenges. However, little is known globally about health problems in these women at midlife and beyond, including menopausal symptoms, sexual dysfunctions and pelvic floor disorders. This scoping review aimed to understand these neglected health issues with respect to prevalence and risk factors. Eight databases were searched in August 2022 without the limit of publication year. Data were analyzed narratively. A total of 10 reports from seven studies were included with 945 women living in Australia, Canada, the USA and Pakistan. Three reports were addressing menopause, seven addressed sexual dysfunctions and one addressed pelvic floor disorders. There were no data regarding menopause symptoms; however, perceptions of menopause varied widely across studies. Few studies reported a high prevalence of sexual dysfunctions and pelvic organ prolapses, but none of them used a validated questionnaire. Taboos and cultural factors, lack of knowledge and education, lack of family support, language insufficiency and financial problems were common barriers to not seeking care for these health issues. This review demonstrates lack of evidence of these neglected health issues in refugee and asylum seeker women at midlife, and further studies with validated questionnaires and larger samples are warranted.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".