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Record W4323649698 · doi:10.2196/40138

Maintaining Adherence to COVID-19 Preventive Practices and Policies Pertaining to Masking and Distancing in the District of Columbia and Other US States: Systematic Observational Study

2023· article· en· W4323649698 on OpenAlexvenueno aff
Monica S. Ruiz, Mercedes V McMahon, Hannah Latif, Amita Vyas

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studySocial distanceFace masksMasking (illustration)Public healthEnvironmental healthPopulationDescriptive statisticsSocial mediaPsychologyData collectionMedicineCoronavirus disease 2019 (COVID-19)GeographyComputer scienceNursingSociologyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Prior to the development of effective vaccines against SARS-CoV-2, masking and social distancing emerged as important strategies for infection control. Locations across the United States required or recommended face coverings where distancing was not possible, but it is unclear to what extent people complied with these policies. OBJECTIVE: This study provides descriptive information about adherence to public health policies pertaining to mask wearing and social distancing and examines differences in adherence to these policies among different population groups in the District of Columbia and 8 US states. METHODS: This study was part of a national systematic observational study using a validated research protocol for recording adherence to correct mask wearing and maintaining social distance (6 feet/1.83 meters) from other individuals. Data were collected from December 2020 to August 2021 by research team members who stationed themselves in outdoor areas with high pedestrian traffic, observed individuals crossing their paths, and collected data on whether individuals' masks were present (visible or not visible) or worn (correctly, incorrectly, not at all) and whether social distance was maintained if other individuals were present. Observational data were entered electronically into Google Forms and were exported in Excel format for analysis. All data analyses were conducted using SPSS. Information on local COVID-19 protection policies (eg, mask wearing requirements) was obtained by examining city and state health department websites for the locations where data were being collected. RESULTS: At the time these data were collected, most locations in our study required (5937/10,308, 57.6%) or recommended (4207/10,308, 40.8%) masking. Despite this, more than 30% of our sample were unmasked (2889/10136, 28.5%) or masked incorrectly (636/10136, 6.3%). Masking policy was significantly related to correct masking with locations that required or recommended masking (66% correct masking vs 28/164, 17.1% in locations that did not require masking, P<.001). Participants who maintained social distance from others were more likely to be correctly masked than those who were not (P<.001). Adherence to masking policy by location was significant (P<.001); however, this was driven by 100% compliance in Georgia, which did not require masks at any point during the data collection period. When the same analysis was conducted for compliance with mask requirements and recommendations, there was no significant difference by location. Overall adherence to masking policies was 66.9. CONCLUSIONS: Despite a clear relationship between mask policies and masking behavior, one-third of our sample was nonadherent to those policies, and approximately 23% of our sample did not have any mask, either on or visible. This may speak to the confusion surrounding "risk" and protective behaviors, as well as pandemic fatigue. These results underscore the importance of clear public health communication, particularly given variations in public health policies across states and localities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.419
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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