Changes in Masking Policies in US Healthcare Facilities in the First Quarter of 2023: Do COVID-19 Cases, Hospitalizations, or Local Political Preferences Predict Loosening Restrictions?
Bibliographic record
Abstract
Abstract Introduction In the first half of 2023, many US healthcare facilities updated their policies on the required use of face masks by patients and employees. Despite statewide lifts of mask mandates in healthcare settings, the decisions by individual healthcare facilities have remained controversial and inconsistent. We sought to understand these decisions by examining COVID-19 case rates, hospitalization rates, and political affiliation, among counties where hospitals reported updated masking policies in news or social media posts. Methods We searched Twitter, Facebook, and Google for news stories related to changes in US healthcare facility masking policies between February 1st and April 30th, 2023. We extracted county-level COVID-19 cases and hospitalizations using data from the CDC and political affiliation was measured using the 2020 presidential election results. We performed logistic regression using COVID-19 cases, hospitalizations, and political affiliation as predictors and a complete lifting of masking requirements as the outcome. Results We found that the odds of lifting the mask requirement was not associated with COVID-19 cases (OR 1.00, 95% CI 0.97 - 1.02, p-value = 0.54), or hospitalizations (OR 1.06 95% CI 0.88-1.27, p-value = 0.33). We found that for every 10% increase in Republican votes in the 2020 presidential election, there was a 1.33 (95% CI 1.07 - 1.64, p-value = 0.01) increase in odds of having lifted masking requirements completely. Discussion We found that the odds of lifting the face mask requirement in healthcare facilities was not associated with COVID-19 cases or hospitalizations but was associated with county-level political affiliation. Our results raise the concern that public health measures may be increasingly seen as political gestures or a response to local political factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".