Shortening the Edinburgh postnatal depression scale using optimal test assembly methods: Development of the EPDS‐Dep‐5
Bibliographic record
Abstract
AIMS: This study used a large database to develop a reliable and valid shortened form of the Edinburgh Postnatal Depression Scale (EPDS), a self-report questionnaire used for depression screening in pregnancy and postpartum, based on objective criteria. METHODS: Item responses from the 10-item EPDS were obtained from 5157 participants (765 major depression cases) from 22 primary screening accuracy studies that compared the EPDS to the Structured Clinical Interview for DSM (SCID). Unidimensionality of the EPDS latent construct was verified using confirmatory factor analysis, and an item response theory model was fit. Optimal test assembly (OTA) methods identified a maximally informative shortened form for each possible scale length between 1 and 9 items. The final shortened form was selected based on pre-specified validity and reliability criteria and non-inferiority of screening accuracy of the EPDS as compared to the SCID. RESULTS: A 5-item short form of the EPDS (EPDS-Dep-5) was selected. The EPDS-Dep-5 had a Cronbach's alpha of 0.82. Sensitivity and specificity of the EPDS-Dep-5 for a cutoff of 4 or greater were 0.83 (95% CI, 0.73, 0.89) and 0.86 (95% CI, 0.80, 0.90) and were statistically non-inferior to the EPDS. The correlation of total scores with the full EPDS was high (r = 0.91). CONCLUSION: The EPDS-Dep-5 is a valid short form with minimal loss of information when compared to the full-length EPDS. The EPDS-Dep-5 was developed with OTA methods using objective, pre-specified criteria, but the approach is data-driven and exploratory. Thus, there is a need to replicate results of this study in different populations.
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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 | 0.015 | 0.046 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".