Genetic and environmental factors predict multivariate trajectories of maternal distress after birth
Bibliographic record
Abstract
Background: Maternal distress influences her own wellbeing and shapes her offspring’s psychosocial adjustment and neurodevelopment across childhood. The aim of this study was to analyze multivariate trajectories of maternal postpartum distress using a latent class modeling approach and to find genetic and psychosocial factors that predict membership within a given group (latent class).Methods: Maternal self-reports of depressive symptoms, parenting stress, general stress, and marital stress were measured at regular intervals during the first six years postpartum in 261 mothers participating in the Maternal Adversity, Vulnerability and Neurodevelopment Study. Genetic risk was determined by calculating a polygenic risk score for Major Depressive Disorder (MDD-PRS). Additionally, we assessed maternal history of early life adversity (mELA), educational level, and prenatal symptoms of depression as psychosocial risk factors. Using Latent Gold® Software, we identified latent classes of mothers based on their 1) average levels of distress and 2) change in distress over time.Results: We identified four latent classes based on average levels of distress and found that class membership probability was influenced by an interaction between MDD-PRS and prenatal depressive symptoms (WaldInteraction(3)=13.19, p=0.004; WaldMDD-PRS(3)=6.02, p=0.11; WaldDepression(3)=41.96; p<0.001), mELA (Wald(3)=8.64, p=0.035), and educational level (Wald(3)=11.61, p=0.009). Furthermore, we found five classes of mothers with distinct across- time trajectories, which were associated with mELA (Wald(3)=12.67, p=0.013).Conclusions: Our findings might become relevant in the clinical setting, e.g. for identifying pregnant women at risk for distress in the postpartum based on her prenatal symptoms of depression and genetic risk, mELA, and educational level.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".