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Promising Practices that show evidence of effectiveness in improving public health outcomes in a specific real-life setting, as indicated by achievement of aims consistent with the objectives of the activities, and are suitable for adaptation by other communities.
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Summary Report/Recommendations
This article discusses the preliminary data regarding Syringe Services Program (SSP) operational and service delivery changes during the response to the COVID-19 pandemic and provides key policy and service provision implications for SSPs. There is an emphasis on the distribution of services provided at SSPs and highlights the need for SSP support in providing education, prevention, and strategies to avoid emerging infectious disease outbreaks.
Best Practices that show evidence of effectiveness in improving public health outcomes when implemented in multiple real-life settings, as indicated by achievement of aims consistent with the objectives of the activities.
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Peer Review Study
This article describes the approach and impact of the Stanford Flu Crew, a service learning program at Stanford University School of Medicine, where pre-clinical students provide vaccines to underserved populations in community settings. The article includes information on both program outcomes (i.e., the number of people vaccinated per year over a 4-year period) and student perceptions of learning outcomes achieved through this program.
Emerging Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and produce early results that are consistent with the objectives of the activities and thus indicate effectiveness.
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Peer Review Study
This is a retrospective cohort study that was used to inform COVID-19 infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach. The strongest predictors of COVID-19 infection were identified as the county’s infection rate and the number of separate units in the NH; other predictors included the county’s population density, historical health deficiencies, and resident density. In addition, the NH’s historical percentage of non-Hispanic white residents was identified as a protective factor. The study concluded that a machine-learning model can help quantify and predict infection risk.