Prenatal mental health data and birth outcomes in the pregnancy during the COVID-19 Pandemic dataset
Bibliographic record
Abstract
The COVID-19 pandemic was a substantial stressor, especially for pregnant individuals. We aimed to understand the impact of COVID-19-related stresses on pregnant individuals and their infants and collected survey-based data across Canada as part of the Pregnancy during the COVID-19 Pandemic (PdP) project. The dataset described here provides baseline prenatal data and basic birth outcomes from PdP participants. This data includes information from pregnant individuals as well as their infants. At enrolment and time of completion of the baseline survey, participants were pregnant, ≥17 years of age, ≤35 weeks of gestation, living in Canada, and able to read and write in English or French. Baseline data were collected between April 2020-April 2021. Infant data were collected between May 2020-December 2021. All data were collected via self-report using online questionnaires in REDCAP. Questionnaires were available in both English and French. Data were checked for completeness and plausibility, and duplicates were removed. The dataset described here includes age, education, and household income of the pregnant individuals reported at the baseline/enrollment survey. Raw scores are provided for the Edinburgh Postnatal Depression Scale (EPDS) and the PROMIS Anxiety scale. Ratings are also given for three variables describing fear of the COVID-19 virus. Birth outcomes are provided for infants, including gestational age at birth, birthweight, length, mode of delivery, and whether the infant spent time in the neonatal intensive care unit (NICU). Delivery date is reported as month and year. These data will be beneficial for anyone interested in researching stress during pregnancy or birth outcomes in the context of the COVID-19 pandemic. They will also be useful to researchers interested in examining more general effects of prenatal distress on birth outcomes in children. Data could also be compared to other datasets from the COVID-19 pandemic to establish generalizability, or to pre-pandemic datasets to determine the extent of changes during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".