Pregnant during the COVID-19 pandemic: an exploration of patients’ lived experiences
Bibliographic record
Abstract
BACKGROUND: Infectious outbreaks are known to cause fear and panic. Exploration of pregnant individuals' psychosocial condition using a qualitative lens during an infectious outbreak is limited. In this study we explore pregnant individuals' lived experiences as well as their psychological and behavioural responses during COVID-19 with the goal of providing useful strategies from the patient's perspective to enable health care providers to help pregnant patients navigate this and future pandemics. METHODS: Pregnant individuals between 20-weeks gestation and 3 months postpartum who received maternity care from an urban academic interprofessional teaching unit in Toronto, Canada were invited to participate. Semi-structured 60 min interviews were audio-recorded, transcribed and analyzed using descriptive thematic analysis. Interview questions probed psychological responses to the pandemic, behavioural and lifestyle changes, strategies to mitigate distress while pregnant during COVID-19 and advice for other patients and the healthcare team. RESULTS: There were 12 participants, mean age 35 years (range 30-43 years), all 1 to 6 months postpartum. Six main themes emerged: 1) Childbearing-related challenges to everyday life; 2) Increased worry, uncertainty and fear; 3) Pervasive sense of loss; 4) Challenges accessing care; 5) Strategies for coping with pandemic stress; 6) Reflections and advice to other pregnant people and health care professionals. Pregnant individuals described lack of social support due to COVID-19 pandemic restrictions and a profound sense of loss of what they thought their pregnancy and postpartum period should have been. Advice to healthcare providers included providing mental health support, clear and up to date communication as well as more postpartum and breastfeeding support. CONCLUSIONS: These participants described experiencing psychosocial distress during their pregnancies and postpartum. In a stressful situation such as a global pandemic, health care providers need to play a pivotal role to ensure pregnant individuals feel supported and receive consistent care throughout the pregnancy and postpartum period. The health care provider should ensure that mental health concerns are addressed and provide postpartum and breastfeeding support. Without addressing this need for support, parental mental health, relationships, parent-infant bonding, and infant development may be negatively impacted.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".