Maternal Perceived Stress During the COVID-19 Pandemic: Pre-Existing Risk Factors and Concurrent Correlates in New York City Women
Bibliographic record
Abstract
Objective: We examined whether pre-pandemic mental health and sociodemographic characteristics increased the susceptibility of pregnant women and mothers of young children to stress in the early months of the COVID-19 pandemic. Methods: Between April and August 2020, we surveyed 1560 women participating in a sociodemographically diverse birth cohort in New York City. Women reported their perceived stress, resiliency, and financial, familial/societal, and health-related concerns. We extracted pre-pandemic information from questionnaires and electronic health records. Results: Pre-pandemic history of depression, current financial difficulties, and COVID-19 infection were the main risk factors associated with high perceived stress. Being Hispanic and having higher resiliency scores and preexisting social support were protective against high perceived stress. Major contributors to current perceived stress were financial and familial/societal factors related to the COVID-19 pandemic. Among pregnant women, changes to prenatal care were common, as were changes to experiences following birth among postpartum women and difficulties in arranging childcare among mothers of young children. Conclusion: Our findings suggest that major risk factors of higher stress during the pandemic were similar to those of other major traumatic events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".