Postpartum people’s experiences of and responses to the COVID-19 pandemic during the first year of the pandemic: A descriptive qualitative study
Bibliographic record
Abstract
BACKGROUND: Most evidence on the impact of pandemic on perinatal population's experiences has reported such effects in a portion of the pandemic. OBJECTIVES: The aim of this study was to understand the postpartum people's experiences of and responses to the coronavirus disease 2019 pandemic during the first year and to identify their health care needs. DESIGN: This is a descriptive qualitative study. METHODS: This study was conducted in British Columbia, Canada, between March 2020 and April 2021. Participants (N = 268) were at 4 months postpartum and were recruited as part of the Pregnancy Specific Anxiety Tool study through prenatal care clinics and classes, community laboratory services and social media. Qualitative data were obtained using six online open-ended questions and were analysed using thematic analysis. RESULTS: Findings were grouped under five central themes: protecting baby (with three categories including hypervigilance, constant decision-making to find balance and developmental issues); psychological adjustments (with three categories including coping, anxiety and grief); experience of isolation and lack/loss of support (with two categories including isolation and loss of expected support); unexpected interruptions and life events (with four categories including interrupted maternity leave, unexpected changes/life events, positive impacts and interruption in health care services); and perceived postpartum care needs (with five categories including in-person visits, allowing support persons, providing information/education/support groups, mental health and social support and pro-active check-ins). CONCLUSION: Several impacts of the pandemic persisted throughout the first year, particularly isolation and lack of support. These findings can inform responsive health care services to address the emerging needs of postpartum people throughout the pandemic.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".