Prenatal Maternal Anxiety in South Asia: A Rapid Best-Fit Framework Synthesis
Bibliographic record
Abstract
Background Most research efforts towards prenatal maternal anxiety has been situated in high-income countries. In contrast, research from low- and middle-income countries has focused on maternal depression and prenatal maternal anxiety in low- and middle-income countries remains poorly understood. Objectives To examine whether current prenatal maternal anxiety measurement tools capture dimensions of anxiety during pregnancy that are experienced by women in South Asia. Design We conducted a rapid review with best fit framework synthesis, as we wished to map study findings to an a priori framework of dimensions measured by prenatal maternal anxiety tools. Data sources We searched MEDLINE, PsycINFO, and CINAHL and grey literature in November 2016. Studies were included if published in English, used any study design, and focused on women’s experiences of prenatal/antenatal anxiety in South Asia. Review methods Study quality was assessed using the Effective Public Health Practice Project Quality Assessment Tool and Critical Appraisal Skills Programme Qualitative Checklist. Study findings were extracted to an a priori framework derived from pregnancy-related anxiety tools. Results From 4177 citations, 9 studies with 19,251 women were included. Study findings mapped to the a priori framework apart from body image. A new theme, gender inequality, emerged from the studies and was overtly examined through gender disparity, gender preference of fetus, or domestic violence. Conclusions Gender inequality and societal acceptability of domestic violence in South Asian women contextualizes the experience of prenatal maternal anxiety. Pregnancy-related anxiety tools should include domains related to gender inequality to better understand their influence on pregnancy outcomes.
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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 | 0.079 | 0.211 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.048 | 0.035 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".