Religiosity, depression, and infertility-related stress in Muslim women struggling with infertility and living in the West
Bibliographic record
Abstract
Infertility is a deeply distressing experience that affects millions of women each year, with many also suffering from depression. Depression may increase the risk of experiencing infertility, and decrease the success rate of infertility treatment. Studies suggest that religiosity/spirituality (R/S) may be protective against depression; however, in the Western world these connections remain largely unexplored outside of Christian populations. This study sought to extend our understanding of the relationship between R/S, depression, and infertility-related stress among Muslim women struggling with infertility and living in a Western context. An ethnically diverse sample of 496 Muslim women residing in Australia, Canada, the United Kingdom, and the United States with self-reported fertility problems was recruited using online platforms. Participants completed an online cross-sectional self-report survey consisting of a sociodemographics questionnaire, the Center of Epidemiologic Studies Depression Scale - Revised (CESD-R), the Fertility Problem Inventory (FPI), the Brief RCOPE, and the Religious Commitment Inventory - 10 (RCI-10). As hypothesized, there was a significant and positive relationship between depression and infertility-related distress. While negative religious coping (NRC) was significantly related to depression and infertility-related stress, positive religious coping (PRC) and religious commitment were not. Findings may guide the development of interventions that improve mental health and increase chances of successful pregnancy among Muslim women with fertility problems. Implications for clinical applications and future research are discussed.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".