Policies and Practices in a Cohort of Mississippi Birthing Hospitals During the COVID-19 Pandemic
Bibliographic record
Abstract
Background and Objectives:Guidance around maternity care practices and infant feeding during the COVID-19 pandemic changed over time and was sometimes conflicting. Hospital maternity practices influence breastfeeding, an important preventive strategy against viral illness. Most birthing hospitals in Mississippi are enrolled in CHAMPS, a quality improvement initiative to support breastfeeding and continuously collect maternity care data. The aims of this study were to (1) assess changes to maternity care policies in response to COVID-19, and (2) compare hospital-level breastfeeding, skin-to-skin, and rooming-in rates, at cohort hospitals, before and during the pandemic, overall and stratified by race. Methods:Hospitals responded to a survey on maternity policies in May and September 2020 (Aim 1); hospitals submitted data on breastfeeding and maternity care practices before and during the pandemic (Aim 2). We tested for differences in survey responses using chi-squared statistics and performed an interrupted time series analysis on breastfeeding and maternity care practices data. Results:Twenty-six hospitals responded to the May and September 2020 surveys. Hospitals used different sources to create maternity care policies, and policies differed between institutions. Trends in rates of any and exclusive breastfeeding in the hospital cohort plateaued during the pandemic, in comparison to previous gains, and rates of skin-to-skin and hospital rooming-in decreased. No differences were evident between races. Conclusions:Policies (Aim 1) and practices in the quality improvement cohort hospitals were inconsistent during the COVID-19 pandemic, and changes measured to practices were detrimental (Aim 2). Ongoing monitoring is recommended.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".