A psychometric systematic review of self‐report instruments to identify anxiety in pregnancy
Bibliographic record
Abstract
AIMS: To report a systematic review of the psychometric properties of self-report instruments to identify the symptoms of anxiety in pregnancy to help clinicians and researchers select the most suitable instrument. BACKGROUND: Excessive anxiety in pregnancy is associated with adverse birth outcomes, developmental and behavioural problems in infants and postnatal depression. Despite recommendations for routine psychological assessment in pregnancy, the optimal methods to identify anxiety in pregnancy have not been confirmed. DESIGN: Psychometric systematic review. DATA SOURCES: A systematic literature search of the multiple databases (1990-September 2014). REVIEW METHODS: Identification of self-report instruments to measure anxiety in pregnancy using COSMIN guidelines to assess studies reporting a psychometric evaluation of validity and reliability. RESULTS: Thirty-two studies were included. Studies took place in the UK, Australia, Belgium, Canada, Germany, Italy, Scandinavia, Spain and the Netherlands. Seventeen different instruments were identified. Measures of validity were reported in 19 papers and reliability in 16. The overall quality of the papers was rated as fair to excellent using the COSMIN checklist. Only one paper scored excellent in more than one category. CONCLUSION: Many instruments have been adapted for use in different populations to those for which they were designed. The State Trait Anxiety Inventory, Edinburgh Postnatal Depression Scale and the Hospital Anxiety and Depression Scale have been tested more frequently than other instruments, yet require further assessment to confirm their value for use in 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 | 0.017 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".