Predictors of COVID-19 Pandemic-Related Pregnancy Stress: Prenatal and Postpartum Experiences in Canada
Bibliographic record
Abstract
The COVID-19 pandemic and related public health and hospital restrictions directly influenced Canadian perinatal healthcare. This study aimed to evaluate predictors of pandemic-related pregnancy and postpartum stress in Canada. A sample of 398 women with Canadian pandemic pregnancy experiences completed an online cross-sectional survey between September 2021 and February 2022. Demographic factors, perinatal healthcare characteristics, and psychometric measures including Oslo Social Support Scale (OSSS-3) and Brief COPE were analyzed by independent hierarchical generalized linear models (GLM) to identify predictive variables associated with prenatal and postpartum pandemic-related pregnancy stress scales (PREPS). Respondents reported low social support, low-moderate Problem-Focused and Emotion-Focused Coping scores, with low Avoidant Coping. Middle income and canceled prenatal care appointments were associated with prenatal PREPS-Preparedness Stress, with provider satisfaction negatively associated. Avoidant Coping was positively associated with both prenatal and postpartum Preparedness Stress and Infection Stress scores, whereas Problem-Focused Coping was associated with both prenatal and postpartum Positive Appraisal. High COVID-19 rates and region of healthcare were associated with prenatal and postpartum Infection Stress. Our findings that perinatal healthcare characteristics and psychometric measures, rather than demographic characteristics, were greater predictors of pandemic-related stress reflect the broad societal disruptions that shaped Canadian pregnancy experiences in our sample of mostly high income, well-educated, non-racialized women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".