Development of a Prenatal Psychosocial Screening Tool for Post‐Partum Depression and Anxiety
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Post-partum depression (PPD) is the most common complication of pregnancy in developed countries, affecting 10-15% of new mothers. There has been a shift in thinking less in terms of PPD per se to a broader consideration of poor mental health, including anxiety after giving birth. Some risk factors for poor mental health in the post-partum period can be identified prenatally; however prenatal screening tools developed to date have had poor sensitivity and specificity. The objective of this study was to develop a screening tool that identifies women at risk of distress, operationalized by elevated symptoms of depression and anxiety in the post-partum period using information collected in the prenatal period. METHODS: Using data from the All Our Babies Study, a prospective cohort study of pregnant women living in Calgary, Alberta (N = 1578), we developed an integer score-based prediction rule for the prevalence of PPD, as defined as scoring 10 or higher on the Edinburgh Postnatal Depression Scale (EPDS) at 4-months postpartum. RESULTS: The best fit model included known risk factors for PPD: depression and stress in late pregnancy, history of abuse, and poor relationship quality with partner. Comparison of the screening tool with the EPDS in late pregnancy showed that our tool had significantly better performance for sensitivity. Further validation of our tool was seen in its utility for identifying elevated symptoms of postpartum anxiety. CONCLUSION: This research heeds the call for further development and validation work using psychosocial factors identified prenatally for identifying poor mental health in the post-partum period.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it