Impact of <scp>COVID</scp>‐19 on perinatal care: Perceptions of family physicians in the United States
Bibliographic record
Abstract
BACKGROUND: Patient-centered care is the best practice in the care of pregnant and postpartum patients. The COVID-19 pandemic prompted changes in perinatal care policies, which were often reactive, resulting in unintended consequences, many of which made the delivery of patient-centered care more difficult. This study aimed to understand the impact of the COVID-19 pandemic on perinatal health care delivery from the perspective of family physicians in the United States. METHODS: From October 5 to November 4, 2020, we surveyed mid- to late-career family physicians who provide perinatal care. We conducted descriptive analyses to measure the impact of COVID-19 on prenatal care, labor and delivery, postpartum care, patient experience, and patient volume. An immersion-crystallization approach was used to analyze qualitative data provided as open-text comments. RESULTS: Of the 1518 survey respondents, 1062 (69.8%) stated that they currently attend births; 595 of those elaborated about the impact of COVID-19 on perinatal care in free-text comments. Eight themes emerged related to the impact of COVID-19 on perinatal care: visitation, patient decisions, testing, personal protective equipment, care continuity, changes in care delivery, reassignment, and volume. The greatest perceived impact of COVID-19 was on patient experience. CONCLUSIONS: Family physicians who provided perinatal care during the COVID-19 pandemic noted a considerable impact on patient experience, which particularly affected the ability to deliver patient-centered and family-centered care. Continued research is needed to understand the long-term impact of policies affecting the delivery of patient-centered perinatal care and to inform more evidence-based, proactive policies to be implemented in future pandemic or disaster situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".