(087) LIFTING THE VEIL ON A TABOO TOPIC: SEXUAL DYSFUNCTION IN MIGRANT AND REFUGEE WOMEN VERSUS AUSTRALIAN-BORN COUNTERPARTS – A NATIONAL SURVEY
Bibliographic record
Abstract
Abstract Introduction Australia’s diverse migrant population necessitates a deeper understanding of migrant health needs, particularly in sexual and reproductive health. Female sexual dysfunction (FSD) significantly impacts quality of life, yet evidence on its prevalence and associated factors among migrant women in Australia is limited. Objective This study aimed to explore FSD prevalence among migrant women from low and middle-income countries (LMICs) residing in Australia, compare FSD prevalence between migrant and Australian-born women, and examine socio-demographic factors associated with FSD in both groups. Methods This national survey included reproductive-aged women (N = 868), comprising migrant women from LMICs (N = 421) and Australian-born women (N = 447). Participants were recruited through quota sampling via the Qualtrics online platform. The study employed the Female Sexual Function Index (FSFI) and a demographic questionnaire. Data analysis involved descriptive statistics, chi-square tests, and logistic regression. Results FSFI domain comparisons revealed significant differences between migrant and Australian-born women, migrant women reported significantly better overall sexual function (24.98 ± 7.18 vs. 23.57 ± 0.96, p = 0.01). Longer relationships were negatively associated with sexual function, while religious affiliation showed a significant impact on sexual dysfunction compared to no religious affiliation. Logistic regression analysis highlighted those high-income migrants had higher odds of better sexual function (OR: 2.27, 95% CI: 1.10-4.66) compared to low-income migrants. Regarding religion, non-religious Australian showed higher odds (OR: 3.24, 95% CI: 1.12-9.32) compared to religious ones. Conclusions This pioneering study highlighted the need for tailored interventions considering socioeconomic status and cultural background, providing a foundation for further research on the intersections of migration, culture, and sexual well-being. This study contributes to a more comprehensive understanding of sexual health in Australia’s multicultural context, promoting overall well-being and quality of life for all women. Disclosure No.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".