The role of perceived social support on pregnant women’s mental health during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: The present study aimed at investigating which sources of social support best account for pregnant women's levels of psychological distress and mental well-being during the COVID-19 pandemic. METHODS: 274 Italian and Canadian expectant mothers completed an online-based survey including measures of perceived social support (from family, significant other and friends), state anxiety, depressive symptoms, and satisfaction with life. Correlation analyses and amultivariate analysis of covariance were performed to explore how social support from different sources was related to depressive symptoms, state anxiety and satisfaction with life. RESULTS: Different sources of social support contributed to explaining women's psychological distress and mental well-being. Social support both from family and friends was significantly related to women's state anxiety and depressive symptoms. Social support from friends was specifically related to women's satisfaction with life. CONCLUSION: Our findings endorse the crucial role of perceived social support as a protective factor for pregnant women's mental health. In the context of the COVID-19 pandemic, our results suggest that support from family seems important in preventing psychological distress, whereas support from friends is also associated with mental well-being. These results may help designing future interventions aimed at improving women's perinatal mental health in life-threatening conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".