The impact of the COVID-19 pandemic on the mental health of women seeking treatment for postpartum depression
Bibliographic record
Abstract
BACKGROUND: While research has examined the mental health of general population samples of postpartum women during the COVID-19 pandemic, the pandemic's impact on women seeking treatment for postpartum depression (PPD) is not well known. This study compared levels of depression and anxiety, the quality of social relationships, and the temperament of infants of treatment-seeking mothers in Ontario, Canada prior to and during the pandemic. METHODS: = 120). Mothers self-reported symptoms of depression, worry/anxiety, partner relationship quality, social support, as well as aspects of the mother-infant relationship and infant temperament. RESULTS: There were no statistically significant differences in symptoms of depression, anxiety, or the quality of social relationships between women seeking treatment for PPD before or during the pandemic. However, mothers reported poorer relationships with their infants, and there was evidence of more negative emotionality in their infants during COVID-19. CONCLUSIONS: The pandemic may not have worsened depression, anxiety, relationships with partners, or social support in mothers seeking treatment for PPD, but appears to have contributed to poorer mother-infant interactions and maternal reports of more negative emotionality in their infants. These findings highlight the importance of identifying women with possible PPD, supporting mother-infant interactions, and monitoring their infants during COVID-19 and beyond.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".