Perinatal Loneliness and Isolation Early in the COVID‐19 Pandemic in New York City: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: During the COVID-19 pandemic, birthing parents were identified as a high-risk group with greater vulnerability to the harms associated with SARS-CoV-2. This led to necessary changes in perinatal health policies but also to experiences of maternal isolation and loneliness, both in hospital settings, due to infection mitigation procedures, and once home, due to social distancing. METHODS: In this study, we qualitatively explored birthing and postpartum experiences in New York City during the early days of the pandemic when lockdowns were in effect and policies and practices were rapidly changing. Using thematic analysis, our focus was on experiences of isolation, navigating these experiences, and the potential impacts of isolation and loneliness on maternal health for 55 birthing people. RESULTS: Participants described numerous stressors related to isolation during the birthing process, including reconciling their hopes for their birth with the realities of the unknown and separation from partners, family, and friends in the hospital. During the postpartum period, loneliness manifested as having limited or no contact with family and friends, which led to feelings of a need for strengthened social support systems. The impact of these negative experiences shaped mental health. Overall, we found that solitary experiences during birthing and postpartum isolation were major sources of stress for participants in this study. DISCUSSION: To support impacted families and prepare for future crisis events, clinicians and researchers must prioritize the development of strong clinical and social support structures for perinatal people to ensure both maternal and child health.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".