Symptoms of postpartum anxiety and depression among women in Canada: findings from a national cross-sectional survey
Bibliographic record
Abstract
OBJECTIVE: This study presents national estimates on symptoms consistent with postpartum anxiety (PPA) and postpartum depression (PPD) and the association between these conditions and possible risk and protective factors in women who gave birth in Canada. METHODS: Data were collected through the Survey on Maternal Health, a cross-sectional survey administered in Canada's ten provinces between November 2018 and February 2019 among women who gave birth between January 1 and June 30, 2018. A total of 6558 respondents were included. Weighted prevalence estimates were calculated, and logistic regression was used to model the relationship between symptoms consistent with PPA, PPD, and potential risk factors. RESULTS: Overall, 13.8% of women had symptoms consistent with PPA, while the prevalence of having symptoms consistent with PPD was 17.9%. Results of the logistic regression models indicated that women who had a history of depression were 3.4 times (95% CI 2.7-4.2) more likely to experience symptoms consistent with PPA and 2.6 times more likely to experience symptoms consistent with PPD (95% CI 2.2-3.2) compared with those who did not. Women who reported good, fair, or poor physical health were 2.4 times more likely to experience symptoms consistent with PPD (95% CI 2.0-2.9) and 2.0 times more likely to experience symptoms consistent with PPA (95% CI 1.7-2.4) compared with those who reported very good or excellent health. Maternal marital status, other postpartum maternal support, and sense of community belonging were also significant. CONCLUSION: This study highlights that a history of depression and good, fair, or poor physical health are associated with an increased odds of symptoms consistent with PPA and PPD, while other maternal support and sense of community belonging are associated with a decreased odds of these 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".