Prediction of post-partum depression and anxiety based on clinical interviews and symptom self-reports of depression and anxiety during pregnancy
Bibliographic record
Abstract
Introduction The tools used to evaluate mental health during pregnancy matter. Their efficacy in identifying symptom severity enables better predictions of postpartum mental health. The Mother & Youth: Research on Neurodevelopment & behaviour (MYRNA) cohort is an NIH funded longitudinal cohort from Sherbrooke, Canada studying the effects of pregnant women’s mental health. Objectives We examine which mental health tools will better gauge depression and anxiety during pregnancy based on predicting postpartum outcomes. Our hypothesis is that an approach combining a clinical interview with self-report questionnaires may predict mental health in postpartum women. Methods Participants’ mental health is evaluated by the SCID-5-RV, a lifetime interview administered at 30 weeks and monthly questionnaires including PHQ-9 and GAD-7. Participants are in the depression/anxiety group if they either pass all the criteria in the SCID during pregnancy or have an average PHQ-9 or GAD-7 score greater than 7. The Edinburgh Postnatal Depression Scale (EPDS) and the Perceived Stress Scale (PSS) are the outcome variables. Results PHQ-9 was correlated with EPDS, r (220)= .38, p < .01, and GAD-7 was correlated with PSS, r (213)= .56, p< .01. SCID results only had a significant effect on PSS, F (3,220)= 3.77, p = .01 and not with EPDS, F (3,219)= 1.08, p = .36. When the self-report measures and interview were combined significant effects were seen for both the EPDS, F (1,222)= 18.71, p < .01 and the PSS, F (1,223)= 34.94, p <.01. Conclusions Preliminary results show significant associations between measures administered during pregnancy and postpartum measures. Prediction models based on classification will be analyzed once more data is collected. Disclosure No significant relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".