The prevalence of anxiety symptoms in infertile women: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Infertile women are exposed more frequently to anxiety risk than are infertile men, thereby adversely affecting the procedures with which they are treated and the quality of their lives. Yet, this problem is often disregarded. This study accordingly determined the prevalence of anxiety symptoms among infertile women. Methods All Persian and English studies published from the early 2000s to May 2019 were searched in international (i.e., PubMed, the Cochrane Library, Web of Science, Scopus, Embase, and PsycINFO) and national (i.e., SID, Magiran) databases as well as through Google Scholar. After the titles and abstracts of the articles were reviewed, their quality was evaluated, and relevant works for examination were selected in consideration of established inclusion and exclusion criteria. The risk of biases of individual studies according to Newcastle - Ottawa Scale was assessed. The heterogeneity of the studies was assessed using the I2 statistic, and indicators of publication bias were ascertained using Egger’s test. Stata (version 14) was employed in analyzing the findings. Results Thirteen studies having a collective sample size of 5055 infertile women were subjected to meta-analysis, with study heterogeneity incorporated into a random effects model. The findings indicated that 36% of the infertile women involved in the evaluated studies self-reported their experience with anxiety. The pooled prevalence of the condition among the subjects was 36.17% [95% confidence interval (CI): 22.47–49.87]. The pooled prevalence levels in low- and middle-income countries and high-income countries were 54.24% (95% CI: 31.86–78.62) and 25.05% (95% CI: 15.76–34.34), respectively. The results revealed no evidence of publication bias (P Egger’s test = 0.406). Conclusion Considering the prevalence of anxiety in infertile women and its effects on health processes and quality of life, this problem requires serious consideration and planning for effective intervention, especially in low- and middle-income nations.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".