Antenatal Depression And Anxiety In Bihar Women: A Systematic Phycological Study
Bibliographic record
Abstract
There is good evidence to suggest that high prevalence of depression and anxiety in the postpartum period. However, very few studies have focused on antenatal depression and anxiety disorders and their associated risk factors. Further, there are only a handful of studies from India on common antenatal mental health disorders. With this background, we reviewed the existing evidence on antenatal depression and anxiety from the studies conducted in Indian pregnant women during the antenatal period and to explore the associated risk factors. All the major databases were searched systematically for English language studies on prevalence and risk factors for antenatal depression and anxiety in Indian pregnant females, published during the period January 2022 to May 2023. Quality assessment of studies was done with the modified version of Newcastle Ottawa Scale for cross-sectional studies. We found the overall prevalence of antenatal depression was ranged from 3.8% to 65% and antenatal anxiety from 13 to 55%. The most relevant risk factors associated with antenatal depression and anxiety during pregnancy were preference to have a male child, intimate partner violence, history of abortions, marital conflict, poor relationship with the husband/in-laws and lack of social support. To conclude, the systematic review suggests that depressive and anxiety disorders are quite common in Indian pregnant women in antepartum period with varying prevalence depending on various settings and scales used. Steps should be taken to promote obstetricians for regular mental health screening during the antenatal visits and prompt referral to mental health professionals when suspected.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".