Measurement of pregnancy-related anxiety worldwide: a systematic review
Bibliographic record
Abstract
BACKGROUND: The perinatal period is often characterized by specific fear, worry, and anxiety concerning the pregnancy and its outcomes, referred to as pregnancy-related anxiety. Pregnancy-related anxiety is uniquely associated with negative maternal and child health outcomes during pregnancy, at birth, and early childhood; as such, it is increasingly studied. We examined how pregnancy-related anxiety is measured, where measures were developed and validated, and where pregnancy-related anxiety has been assessed. We will use these factors to identify potential issues in measurement of pregnancy-related anxiety and the geographic gaps in this area of research. METHODS: We searched the Africa-Wide, CINAHL, MEDLINE, PsycARTICLES, PsycINFO; PubMed, Scopus, Web of Science Core Collection, SciELO Citation Index, and ERIC databases for studies published at any point up to 01 August 2020 that assessed pregnancy-related anxiety. Search terms included pregnancy-related anxiety, pregnancy-related worry, prenatal anxiety, anxiety during pregnancy, and pregnancy-specific anxiety, among others. Inclusion criteria included: empirical research, published in English, and the inclusion of any assessment of pregnancy-related anxiety in a sample of pregnant women. This review is registered on PROSPERO (CRD42020189938). RESULTS: The search identified 2904 records; after screening, we retained 352 full-text articles for consideration, ultimately including 269 studies in the review based on the inclusion and exclusion criteria. In total, 39 measures of pregnancy-related anxiety were used in these 269 papers, with 18 used in two or more studies. Less than 20% of the included studies (n = 44) reported research conducted in low- and middle-income country contexts. With one exception, all measures of pregnancy-related anxiety used in more than one study were developed in high-income country contexts. Only 13.8% validated the measures for use with a low- or middle-income country population. CONCLUSIONS: Together, these results suggest that pregnancy-related anxiety is being assessed frequently among pregnant people and in many countries, but often using tools that were developed in a context dissimilar to the participants' context and which have not been validated for the target population. Culturally relevant measures of pregnancy-related anxiety which are developed and validated in low-income countries are urgently needed.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".