Psychological Distress in Childbearing Persons During the COVID‐19 Pandemic: A Multi‐Trajectory Study of Anger, Anxiety, and Depression
Bibliographic record
Abstract
Psychological distress can manifest as depression, anxiety, and anger in the perinatal period. These conditions are often comorbid yet studied in isolation. A full understanding of perinatal psychopathology requires the spectrum of common psychological distress to be studied concurrently to better understand interconnected symptoms. A transdiagnostic approach provides valuable insights into how symptoms interact and cumulatively affect mental health, which can inform more effective screening and treatment strategies. This, in turn, can improve outcomes for birthing parents experiencing psychological distress. We undertook group‐based multi‐trajectory modeling (GBMTM) to uncover the patterns of affective disorders (anger, anxiety, and depression) over three‐time points (pregnancy, 3‐, and 12‐months postpartum (mPP)) in a large longitudinal cohort of persons who gave birth during the COVID‐19 pandemic ( n = 2145). We identified five trajectory groups: high‐stable (11.3%), postpartum‐increase (16.0%), postpartum‐decrease (21.5%), low‐stable (37.9%), and minimal stable (13.2%) symptoms of anger, anxiety, and depression. Multinomial regression revealed that lower levels of sleep disturbance, less financial hardship, and lower intolerance of uncertainty predicted postpartum decreases in psychological distress compared with the high stable group. Higher levels of sleep disturbance, greater financial hardship, lower level of social support, and greater intolerance of uncertainty predicted postpartum increases in psychological distress compared with the low‐stable and minimal‐stable groups. Screening for psychological distress symptoms (i.e., anger, anxiety, and depression), paired with access to evidence‐based management for those who screen positive, is warranted during the first postpartum year to reduce the harmful effects of unmanaged distress on 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".