Romantic Relationship Quality and Mental Health in Pregnancy During the Covid-19 Pandemic
Bibliographic record
Abstract
Introduction: Social capital is important for good mental health and the quality of close relationships is one key indicator of social capital. Examining the association between relationship quality and mental health may be particularly important during pregnancy as mental health concerns during this period pose significant risk to families. The COVID-19 pandemic has contributed to increased mental health problems among pregnant individuals. The resulting lockdown protocols of the pandemic have also disrupted larger social networks and couples spent more time together in the context of ongoing chronic stress, highlighting the particular importance of romantic relationship quality. This study explored longitudinal associations between relationship satisfaction, depression, and anxiety among pregnant individuals during the first wave of the COVID-19 pandemic. Methods: Pregnant individuals (n = 1842) from the Pregnancy During the Pandemic Study were surveyed monthly (April-July 2020). Depression, anxiety symptoms, and relationship satisfaction were self-reported. Cross-lagged panel models were conducted to examine bidirectional associations between relationship satisfaction and mental health symptoms over time. Results: Relationship satisfaction was significantly correlated with depression and anxiety at all time points. Longitudinally, relationship satisfaction predicted later depression and anxiety symptoms, but depression and anxiety symptoms did not predict later relationship satisfaction. Discussion: This study suggests that poor relationship satisfaction was linked to subsequent elevations in prenatal depressive and anxiety symptoms during the COVID-19 pandemic. Relationship enhancement interventions during pregnancy may be a means of improving the mental health of pregnant individuals, and interrupting transgenerational transmission, during times of prolonged psychological distress.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".