Editorial: The Global Impacts of COVID-19 on Maternity Care Practices and Childbearing Experiences
Bibliographic record
Abstract
This special issue onThe Global Impact of COVID-19 on Maternity Care Practices and Childbearing Experiences includes articles that describe the experiences of providers and childbearers in relation to pregnancy, childbirth, and the postpartum period during the COVID-19 pandemic across a range of countries, including the United States, Canada, Mexico, Chile, Italy, Russia, India, Pakistan, Kenya, and New Zealand, as well as an article on pandemic doula care across 23 high-and middle-income countries.Most of the articles in this collection primarily examine the COVID-19 pandemic either from the perspective of providers-including midwives, doulas, obstetricians, nurses, social workers, and other birthworkers-or from the perspective of childbearers.We begin this Editorial by focusing mostly on providers, then turn to childbearers' experiences.All references without dates refer to articles in this Special Issue.These articles cumulatively emphasize that the coronavirus pandemic has revealed and highlighted deep fragmentations, inequalities, and dysfunctions within maternity care that existed before the pandemic began.Indeed, this pandemic offers both a disruptive moment and a long-overdue opportunity to fix systemic problems within maternity care in ways that can benefit providers, mothers, newborns, and families (Gutschow et al., 2021).In short, the pandemic offers an opportunity to shift maternity care toward justice, equality, and human rights for all, as we will further address in our Conclusion to this Editorial. PROVIDERS' ADAPTIVE RESPONSES TO SHIFTING EVIDENCE: COVID-19 AND SARS-COV-2Risk and fear were major themes for providers working within the rapidly evolving situation of COVID-19, in which basic knowledge about the virus, SARS-CoV-2, and the disease it causes, COVID-19, were rapidly evolving during much of 2020 and 2021.As we illustrate (Gutschow and Davis-Floyd), providers were responding to very limited or unproven "evidence" about routes and risks of transmission, including understanding viral loads; how to estimate and mitigate widespread asymptomatic community transmission; and estimating case fatality rates and the progress of the disease-especially for pregnant people.In the early weeks and months of the pandemic, providers
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".