Perinatal Depression in Australian Women during the COVID-19 Pandemic: The Birth in the Time of COVID-19 (BITTOC) Study
Bibliographic record
Abstract
The COVID-19 pandemic has impacted perinatal mental health globally. We determined the maternal factors and pandemic-related experiences associated with clinically significant perinatal (pregnant and post-partum) depressive symptoms in Australian women. Participants (n = 2638; pregnant n = 1219, postnatal n = 1419) completed an online survey (August 2020 through February 2021) and self-reported on depression, social support, and COVID-19 related experiences. We found elevated depressive symptoms amongst 26.5% (pregnant) and 19% (postnatal) women. Multiple logistic regression analyses showed higher likelihood of elevated depression associated with residence in Victoria, lower education, past/current mental health problems, greater non-pandemic prenatal stress, age ≥ 35 years (pregnant women) and existing physical health issues or disability in self or others (postnatal women). Greater family stress/discord and lower social support (friends) was associated with higher odds of elevated perinatal depression, while lower social support (family) was significantly associated with elevated depressive symptoms in pregnant women. Greater depression was associated with social distancing, pandemic-related news exposure and changes to prenatal care (pregnant women). Single postnatal women showed lower odds of elevated depression than partnered women. Our findings underscore the importance of universal screening for depression and targeted support during a pandemic for perinatal women displaying vulnerability factors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".