Maternal Fear of COVID-19 and prevalence of postnatal depression symptoms: Risk and protective factors
Bibliographic record
Abstract
Objective: To evaluate the relations between Fear of COVID-19 and postpartum depression (PPD) symptoms. Design: A multicenter prospective observational study. Setting and Population: A cohort of women who delivered during COVID-19 pandemic between 03-05/2020. Methods: Participants were virtually approached after delivery and asked to complete an online questionnaire. Data was verified with each center’s perinatal database. The validated Fear of COVID-19 Scale was in use. PPD was evaluated using the EPDS questionnaire as a categorical (≥10) and as a continuous scale. Pre-existing maternal disability was defined as any prior physiological/psychological chronic health condition. Stress-contributing complications during pregnancy or at birth included pregnancy and labor related complications. Regression analysis and ROC statistics were utilized to evaluate associations and control for confounders. Main Outcome Measure: PPD symptoms. Results: Overall, 421 women completed the questionnaires. Of them, 99(23.5%) had a high EPDS score. Fear of COVID-19 was positively correlated with PPD symptoms (r=0.35,p=0.000),ROC-AUC 0.67, 95%CI 0.61-0.74. Following adjustment to confounders (maternal age, nulliparity, ethnicity, marital status, financial difficulties, maternal disability, accessibility to medical services, and stress-contributing complications during pregnancy (, the most important factor that correlated with depression was maternal disability (aOR3,95%CI 1.3-6.9) followed by Fear of COVID-19 (aOR1.1,95%CI 1.05-1.15). High accessibility to medical services (aOR0.59,95% CI 0.45-0.77) and stress-contributing complications during pregnancy (aOR0.2, 95% CI 0.11-0.82) were both protective for PPD symptoms. Conclusions: During the COVID-19 pandemic, maternal disability and Fear of COVID-19 are positively associated with a high EPDS score. High medical accessibility was found as a protective factor for PPD.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".