Anxiety among women experiencing medically complicated pregnancy: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Symptoms of anxiety are common among pregnant and postpartum women, and 15%-20% of pregnancies are affected by medical complications. Despite this, little is known about the relationship of medical complications in pregnancy and women's experience of anxiety. The purpose of this research was to conduct a systematic review and meta-analysis of differences in anxiety symptom severity among women experiencing a medically complicated versus a medically uncomplicated pregnancy. METHODS: This work was guided by the PRISMA reporting process. Electronic databases MEDLINE and PsycINFO were searched to identify studies that met the inclusion criteria. An adaptation of the Newcastle-Ottawa Quality Assessment Scale for case-control studies was used to perform a quality assessment review. A random-effects meta-analysis was used to calculate the estimated average standardized mean differences. RESULTS: Based on the five studies which met our inclusion criteria, findings provide evidence of higher levels of anxiety symptoms among pregnant women experiencing a medically complicated versus a medically uncomplicated pregnancy. Despite considerable heterogeneity, all mean difference estimates are in the direction of greater anxiety in the high-risk groups. CONCLUSIONS: Women experiencing a medically complex pregnancy report higher levels of anxiety symptoms compared to women experiencing a medically uncomplicated pregnancy.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".