Prevalence of Maternal Depressive Symptoms in Canada during the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the prevalence of depressive symptoms among persons who gave birth in Canada during the COVID-19 pandemic, and to identify the factors associated with these symptoms. METHODS: A cross-sectional survey was conducted between February and April 2021 across Canada. Persons who gave birth during the COVID-19 pandemic were recruited through social media sites and completed an online survey. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS). RESULTS: The survey was initiated by 4828 persons, and 3817 participants were included in the analysis. The mean EPDS score was 10.41 (SD = 5.66). Almost one-half (47.5%) of the participants showed a high level of depressive symptoms (EPDS ≥11). Participants in Atlantic (52.5%) and Eastern (51.2%) Canada were more likely to have depressive symptoms compared with those in the Prairie region (45.2%) and Western Canada (41.2%) (P < 0.001). The risk of depressive symptoms was higher among participants who gave birth earlier in the pandemic and among those who were more socioeconomically disadvantaged. CONCLUSIONS: This study shows that a substantial proportion of participants had high levels of depressive symptoms and that participants with older infants and those living in Atlantic and Eastern Canada had higher mean EPDS scores. Assessment of maternal mental health is important during times of societal disruptions such as pandemics. Proactive mental health assessment and support should be provided to all postpartum persons, especially those with lower socioeconomic and educational resources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".