The Impact of COVID-19 on Maternal Mental Health during Pregnancy: A Comparison between Canada and China within the CONCEPTION Cohort
Bibliographic record
Abstract
The effect of the COVID-19 pandemic on maternal mental health has been described in Canada and China but no study has compared the two countries using the same standardized and validated instruments. In this study, we aimed to evaluate and compare the impact of COVID-19 public health policies on maternal mental health between Canada and China, as we hypothesize that geographical factors and different COVID-19 policies are likely to influence maternal mental health. Pregnant persons >18 years old were recruited in Canada and China using a web-based strategy. All participants recruited between 26 June 2020 and 16 February 2021 were analyzed. Self-reported data included sociodemographic variables, COVID-19 experience and maternal mental health assessments (Edinburgh Perinatal Depression Scale (EPDS), Generalized Anxiety Disorders (GAD-7) scale, stress and satisfaction with life). Analyses were stratified by recruitment cohort, namely: Canada 1 (26 June 2020–10 October 2020), Canada 2 and China (11 October 2020–16 February 2021). Overall, 2423 participants were recruited, with 1804 participants within Canada 1, 135 within Canada 2 and 484 in China. The mean EDPS scores were 8.1 (SD, 5.1) in Canada 1, 8.1 (SD, 5.2) in Canada 2 and 7.7 (SD, 4.9) in China (p-value Canada 2/China: p = 0.005). The mean GAD-7 scores were 2.6 (SD, 2.9) in China, 4.3 (SD, 3.8) in Canada 1 (p < 0.001) and 5.8 (SD, 5.2) in Canada 2 (p < 0.001). When adjusting for stress and anxiety, being part of the Chinese cohort significantly increased the chances of having maternal depression by over threefold (adjusted OR 3.20, 95%CI 1.77–5.78). Canadian and Chinese participants reported depressive scores nearly double those of other crises and non-pandemic periods. Lockdowns and reopening periods have an important impact on levels of depression and anxiety among pregnant persons.
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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 | 0.003 |
| 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 | 0.000 | 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".