The prevalence of depression symptoms among infertile women: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Infertile women’s mental health problems, including depression, are key fertility health issues that affect infertile women more severely than infertile men. Depression may threaten the health of individuals and reduce the quality of their lives. Considering the role and impact of depression on responses to infertility treatments, a systematic review and meta-analysis were conducted to investigate the prevalence of depression symptoms among infertile women. Methods International databases (PubMed, Cochrane Library, Web of Sciences, Scopus, Embase, and PsycINFO), national databases (SID and Magiran), and Google Scholar were searched by two independent reviewers for articles published from 2000 to April 5, 2020. The search procedure was performed in both Persian and English using keywords such as “depression,” “disorders,” “infertility,” “prevalence,” and “epidemiology.” The articles were evaluated in terms of their titles, abstracts, and full texts. The reviewers evaluated the quality of the articles using the Newcastle–Ottawa Scale, after which they analyzed the findings using STATA version 14. The I2 and Egger’s tests were performed to examine heterogeneity and publication bias, respectively. Results Thirty-two articles were subjected to the meta-analysis, and a random effects model was used in the examination given the heterogeneity of the articles. The samples in the reviewed studies encompassed a total of 9679 infertile women. The lowest and highest pooled prevalence rates were 21.01% (95% confidence interval [CI]: 15.61–34.42), as determined using the Hospital Anxiety and Depression Scale, and 52.21% (95% CI: 43.51–60.91), as ascertained using the Beck Depression Inventory, respectively. The pooled prevalence values of depression among infertile women were 44.32% (95% CI: 35.65–52.99) in low- and middle-income countries and 28.03% (95% CI: 19.61–36.44) in high-income countries. Conclusion The prevalence of depression among infertile women was higher than that among the general population of a given country. Especially in low- and middle-income countries, appropriate measures, planning, and policy that target the negative effects of depression on infertile women’s lives should be established to reduce related problems.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".