The Montreal Antenatal Well-Being Study (MAWS): a prospective longitudinal study of perinatal mental health.
Bibliographic record
Abstract
Objective: This prospective longitudinal cohort aims to identify biological, psychological, and social factors that contribute to maternal perinatal mental health, family well-being, and child development. Method: Pregnant individuals (N=1130) were recruited between 8-20 gestation weeks. Questionnaire data were collected through a web-based platform together with biosamples for genetic analysis. Baseline characteristics of the cohort are described. A Bayesian model explored potential pandemic-associated changes in baseline maternal mental health symptoms throughout recruitment. Results: At baseline, 28.3% and 11.6% of pregnant participants reported clinically significant symptoms of anxiety (Spielberger Trait Anxiety Inventory ≥ 40) or depression (Edinburgh Postnatal Depression Scale ≥ 13). The onset of the COVID-19 pandemic was associated with increased likelihood of elevated scores on brief screening instruments for anxiety and depression. There was insufficient evidence for such effects using other screening tools. Conclusion(s): We further highlight anxiety and depression as common complications of pregnancy but find a modest impact of the pandemic on mental health within this cohort. Leveraging the unique data collected through this study we seek to inform screening practices and health policy to improve the well-being of mothers and families.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".