Depression, Anxiety, and Stress in Kazakhstani Women with Recurrent Pregnancy Loss: A Case–Control Study
Bibliographic record
Abstract
Background: Recurrent pregnancy loss (RPL) is associated with increased incidence and severity of depression, anxiety, and stress, and screening for these comorbidities following miscarriages is beneficial for women with RPL who are planning future pregnancies. This study aims to investigate depression, anxiety, and stress among Kazakhstani women with RPL. Methods: This was a case−control study involving 70 women with confirmed RPL and 78 ethnically matched control women. Depression, anxiety, and stress were evaluated using the Depression Anxiety Stress Scales (DASS)-21 instrument. Linear regression and correlation analysis were used in assessing the association of RPL with symptoms of depression, and/or anxiety, and/or stress, after adjusting for key covariates. Results: Women with RPL were found to have significantly higher mean scores for depression (p < 0.001), anxiety (p < 0.001), and stress (p < 0.001) symptoms. Mild−moderate stress and mild−moderate and severe−extreme depression and anxiety symptoms were more frequent in the RPL group than in the control group. Regression analysis demonstrated that RPL was the only significant variable associated with anxiety, depression, and stress symptoms. Conclusion: The results of this study suggest that women with RPL are more likely to experience heightened symptoms of depression, anxiety, and stress. Proper psychological counseling is recommended for women with RPL, as well as their spouses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".