Pregnant people’s responses to the COVID-19 pandemic: a mixed-methods, descriptive study
Bibliographic record
Abstract
BACKGROUND: Given the extent of the COVID-19 pandemic and uncertainty around the timing of its containment, understanding the experiences and responses of the perinatal population is essential for planning responsive maternity care both during and after the pandemic. The aim of this study was to explore the experiences of pregnant people and their responses to the COVID-19 pandemic, and to identify how health care providers can support this population. METHODS: This was a mixed-methods, descriptive study with cross-sectional and qualitative descriptive components. We conducted the study between Mar. 20 and May 31, 2020, in British Columbia, Canada. Any pregnant person at any gestational age living in BC was eligible to participate. We collected quantitative data using online survey tools, including the Edinburgh Postnatal Depression Scale (EPDS) and the 7-item Generalized Anxiety Disorder questionnaire (GAD-7). We collected qualitative data using open-ended questions to explore people's thoughts, feelings and experiences during the pandemic. Participants were recruited using study posters distributed via prenatal care clinics and classes, LifeLabs and social media across the province. We used thematic and descriptive analyses to analyze the data, and we integrated the qualitative and quantitative findings at the interpretation level. RESULTS: = 34) reported anxiety and depressive symptoms, as measured by the GAD-7 and EPDS, respectively. Overarching themes that emerged from the qualitative data included uncertainty about birth plans and setting, added burden to existing health and social disparities, perceived or projected lack of support or limited support, concerns about early development, and struggle over managing multiple demands. Perceived maternity care needs included mental health support, maintaining prenatal care, frequent and proactive check-ins to build rapport, and recommendations specific to the pandemic. INTERPRETATION: We found that the impact of the COVID-19 pandemic on the pregnant population has been substantial. The findings of this mixed-methods study can be used to help plan informed and evidence-based health care interventions to mitigate adverse effects and support 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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".