Pregnancy-Specific Anxiety Tool (PSAT)
Bibliographic record
Abstract
Pregnancy-specific anxiety (PSA) is a distinct construct from general anxiety and depression. The purpose of this study was to develop, evaluate, and validate the Pregnancy-Specific Anxiety Tool (PSAT), to measure PSA and its severity. The study was carried out in 2 stages. Stage 1 involved item development and content and face validation. Stage 2 included psychometric evaluation to examine item distributions and correlational structure, dimensionality, internal consistency reliability, stability, and construct, convergent, and criterion validity, using 2 independent samples (initial sample N = 494, May-October 2018; validation sample N = 325, July 2019-May 2020). Eighty-two items were evaluated for face validity and 41 items were considered in stage 2 based on feedback from participants and experts. Model fit from exploratory factor analysis and patterns of item-factor loadings suggested a 6-factor model with 33 items. The 6 factors included items pertaining to health and well-being of the baby, labor and the pregnant person's well-being, postpartum, support, career and finance, and indicators of severity. Confirmatory factor analysis carried out using the initial sample showed good fit with the validation sample. The area under the curve (AUC) for the diagnosis of adjustment disorders (AD) was 0.73 (95% CI, 0.67-0.79), and for AD/any anxiety disorders, the AUC was 0.80 (95% CI, 0.75-0.85). The PSAT can be useful for screening and monitoring of PSA, and pregnant people with scores higher than 10 should be considered for further assessment.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".