No abstract

Institutions of higher education increasingly have adopted high-impact practices (HIPs) as a means to increase student retention and engagement. Yet, the growth and assessment of these practices have primarily focused on a student’s first and final years, which can contribute to a second-year slump. Administrative data from a small, 4-year liberal arts college are analyzed to investigate when students participate in HIPs and whether this timing affects first and second year retention decisions. Propensity score matching is used to control for selection in which students participate in HIPs. Consistent with previous research, results suggest that HIP participation is a significant predictor of both first and second year retention, though the effect of HIP participation is overestimated without properly controlling for selection bias. Results suggest that strategic incorporation of HIPs in the second year may improve retention outcomes.

No abstract

Background Mass incarceration, commonly associated with overcrowding and inadequate health resources for incarcerated people, creates a fertile environment for the spread of the coronavirus disease 2019 (COVID-19) in U.S. correctional facilities. The exact role that correctional facilities play in enhancing COVID-19 spread and enabling community re-emergence of COVID-19 is unknown. Methods We constructed a novel stochastic model of COVID-19 transmission to estimate the impact of correctional facilities, specifically jails and state prisons, for enhancing disease transmission and enabling disease re-emergence in local communities. Using our model, we evaluated scenarios of testing and quarantining infected incarcerated people at 0.0, 0.5, and 1.0 times the rate that occurs for infected people in the community for population sizes of 5, 10, and 20 thousand people. Results Our results illustrate testing and quarantining an incarcerated population of 800 would reduce the probability of a major outbreak in the local community. In addition, testing and quarantining an incarcerated population would prevent between 10 to 2640 incidences of COVID-19 per year, and annually save up to 2010 disability-adjusted life years, depending on community size. Conclusions Managing COVID-19 in correctional facilities is essential to mitigate risks to community health, and thereby stresses the importance of improving the health standards of incarcerated people.

Background: Use of artificial intelligence-guided echocardiography (AI echo) may increase access to imaging, but there is little experience of its application in rural and low resource settings. The Risk Underlying Rural Areas Longitudinal (RURAL) cohort is the first NHLBI population-based cohort study to employ AI echo. Hypothesis: AI echo is feasible and produces adequate quality images to assess cardiac structure and function among rural populations. Methods: The RURAL study, in partnership with Caption Health (Brisbane, CA), is using AI echo in a multiethnic cohort of 4600 participants, performed in a mobile exam unit (MEU) in 10 rural U.S. communities. Non-sonographer MEU technicians underwent 10 hrs in-person competency-based training before scanning. Cardiac structure and function were analyzed in an independent core laboratory. Ejection fraction (EF) was visually estimated and calculated using Caption Health’s Auto EF technology. Results: Overall, 138 participants had AI echoes analyzed from Sept 2021 to May 2022 of whom 62% were women, with 70% obese (body mass index (BMI) ≥ 30kg/m 2 ), median BMI 33.8kg/m 2 (Table 1). Median time per scan was 20.0mins (15.5, 29.7). Image quality was adequate for visual EF in 97%, with left ventricular dimensions measurable in 88% and left atrial diameter in 91%. Adequate images of the right heart and ascending aorta were less common: base of the right ventricle measurable in 62%, right atrium 61% and ascending aorta 60%. Most participants (96%) had LVEF ≥ 50% by visual estimation, and there was 96% agreement between visual and Auto EF for this group. Image quality, but not measurability, varied with BMI. Conclusion: AI echo imaging technology can be used by non-sonographers to acquire adequate quality images characterizing cardiac structure and function in a rural and predominantly obese population, suggesting utility across the spectrum of BMI and applicability in low resource environments with limited access to healthcare.