Childhood trauma and maternal perinatal depression during COVID-19: A stress sensitization hypothesis
Bibliographic record
Abstract
BACKGROUND: Perinatal depressive symptoms (PDS) are a risk factor for maternal well-being during and following pregnancy as well as for infant development. COVID studies documented a definite increase in PDS during this period of heightened stress, but also highlighted that all women were not equally at risk of perinatal depression. This calls for the identification of factors that could contribute to sensitizing certain individuals to populational stressors such as the COVID-19 pandemic. OBJECTIVE: Based on the stress sensitization model, this study aimed to evaluate the associations between childhood trauma (CT) and depressive symptoms in pregnant women during the COVID-19 pandemic at four timepoints (two prenatal and two postnatal). METHODS: A sample of Canadian mothers (N = 117, Mage = 29.77 years, SD = 3.18, 63.2 % primiparous, 98.3 % White, 23.1 % with history of CT) completed self-reported measures of CT (CTQ) and depressive symptoms (EPDS) during the first or second (T1) and the third trimester of pregnancy (T2), as well as at 2 months (T3) and 6 months (T4) postpartum. Structural equation modeling (SEM) analyses were performed using MPlus. RESULTS: Maternal severity of CT was directly associated with pre- and postnatal depressive symptoms during the COVID-19 pandemic. CT was also indirectly associated with postnatal depressive symptoms via prenatal depressive symptoms. CONCLUSIONS: CT had an enduring association with postnatal depressive symptomatology in part due to its role in prenatal depression during the first COVID-19 outbreak. The implications of the results for perinatal care will be discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".