Reliability and validity of three shortened versions of the State Anxiety Inventory scale during the perinatal period
Bibliographic record
Abstract
The screening for anxiety in obstetric settings has been challenging due to time and knowledge constraints. Brief, valid, and reliable instruments can provide health care professionals with a quick and easy method to assess anxiety. Three six-item forms of the State Anxiety Inventory scale have been constructed. The purpose of this study was to evaluate and compare the psychometric properties of these short versions in the perinatal period. Data were drawn from a longitudinal pregnancy cohort in Alberta, Canada. Internal consistency of the shortened versions was assessed. Confirmatory factor analysis was conducted to estimate and compare indicators of fit during pregnancy and at 4 and 12 months postpartum. All shortened scales demonstrated high internal consistency and reliability, with alphas ranging from 0.81 to 0.85. All fit indices were greater than 0.93, implying a good fit between each model and our data. In the model comparisons, the Marteau and Bekker scale provided a more robust fit to data obtained during pregnancy and the early postpartum period. At 12 months postpartum, the Chlan et al. form demonstrated the best fit of the three versions. The shortened scales appear to have acceptable psychometric properties. Brief scales have the potential to provide an economical means of assessing perinatal anxiety and can be considered as equivalent alternatives to the full-scale version.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".