P1-234 Prenatal screening for suboptimal mental health in the postpartum period
Bibliographic record
Abstract
Postpartum depression (PPD) is the most common complication of pregnancy in developed countries, affecting 10%–15% of all new mothers. There9s been a shift in thinking less in terms of PPD per se to poor psychosocial and transitioning outcomes after giving birth. The objective of this study was to develop a screening tool that identifies women at risk of distress in the postpartum period using information collected prenatally. We used data collected for the All Our Babies Study, a prospective cohort study of pregnant women living in Alberta, Canada (N=1578) that collects a diverse array of information at three time points during the perinatal period. We developed the tool using 2/3 of the sample and performed internal validation on the remaining 1/3 using a regression coefficient-based scoring method. The best fit model included known risk factors for PPD and suboptimal psychosocial health: depression and stress in late pregnancy, history of abuse, and poor relationship quality with partner. The area under the ROC curve was 0.76, with acceptable sensitivity and specificity for a cut-off score of 2 (range 0–7). Comparison of the tool with a widely used PPD screening inventory showed that our tool had better performance indicators. Further validation of our tool for psychosocial distress was seen in its utility for identifying symptoms of anxiety, in addition to depression, at 4 months. There is an opportunity for early detection of risk to inform the development of interventions to prevent difficulties and promote optimal well-being for mothers and their families.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".