Trajectories of anxiety and depression and association with pain in the perinatal and postpartum period
Bibliographic record
Abstract
INTRODUCTION: Mental health conditions are a growing epidemic, with anxiety and depressive disorders ranked as the most prevalent globally. The prevalence of maternal anxiety and depression vary greatly in the literature, with reports of mood differences commonly associated with pregnancy. However, transient mental health changes reported during pregnancy may change over time, and persist into the postpartum. OBJECTIVES: To examine trajectories of anxiety and depression throughout pregnancy and into the early post-partum and establish predictors of trajectory membership including sociodemographic and mental health symptomology. METHODS: One hundred and forty-two pregnant women were assessed at four time points with measures for anxiety, depression, pregnancy-related anxiety, pain intensity, pain catastrophizing, pain interference, and insomnia. Women completed the first survey prior to 20-weeks' gestation and were reassessed every 10-weeks. Surveys were completed on average at 15-weeks', 25-weeks', and 35-weeks' gestation, and at 6-weeks postpartum. Using latent class mixed models, trajectory analysis was utilized to determine maternal trajectories of anxiety and depression. RESULTS: Two-class anxiety and depression models were identified. 58 % and 62 % of mothers were assigned to the elevated subclinical anxiety and elevated subclinical depression trajectories respectively. Adaptive lasso and imputation demonstrated model robustness. Individual associations with trajectories included baseline symptoms of anxiety, depression, insomnia, and pain symptomology. CONCLUSION: These findings identified women with varying anxiety and depression experience during pregnancy and associated predictors of elevated subclinical symptomology. These findings may identify mothers at risk for developing mental health disorders during their pregnancy and into the postpartum period.
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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 | 0.000 |
| 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 | 0.000 |
| 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".