ABSTRACT
Societal change is often driven by shifts in public opinion. As citizens evolve in their norms, beliefs, and values, public policies change too. While traditional opinion polling and surveys can outline the broad strokes of whether public opinion on a particular topic is changing, they usually cannot capture the full multi-dimensional richness and diversity of opinion present in a large heterogeneous population. However, an increasing fraction of public discourse about public policy issues is now occurring on online platforms, which presents an opportunity to measure public opinion change at a qualitatively different scale of resolution and context. In this paper, we present a conceptual model of observed opinion change on online platforms and apply it to study public discourse on Universal Basic Income (UBI) on Reddit throughout its history. UBI is a periodic, no-strings-attached cash payment given to every citizen of a population. We study UBI as it is a clearly-defined policy proposal that has recently experienced a surge of interest through trends like automation and events like the COVID-19 pandemic. We find that overall stance towards UBI on Reddit significantly declined until mid-2019, when this historical trend suddenly reversed and Reddit became substantially more supportive. Using our model, we find the most significant drivers of this overall stance change were shifts within different user cohorts, within communities that represented similar affluence levels, and within communities that represented similar partisan leanings. Our method identifies nuanced social drivers of opinion change in the large-scale public discourse that now regularly occurs online, and could be applied to a broad set of other important issues and policies.
Subject(s)
COVID-19 , StrokeABSTRACT
Since the onset of the COVID-19 pandemic in March 2020, educators grapple with the challenges of creating meaningful content in online courses in higher education. Specifically, designing an online curriculum that increases student motivation, student engagement, and student learning. Learner–Learner (LL) interactions are a key component in not only face-to-face learning, but also online learning. The purpose of this article to share the importance of LL interaction via best practices, examples, and applications in the context of online sexuality education for P–12. However, these ideas for integration of LL interaction best practices and strategies are suitable and can be adjusted for a wide variety of online courses, across all grade levels. The authors share three core practice principles that focus on nurturing LL interactions in online courses: 1) Netiquette expectations, 2) Students’ independent channels of communications, and 3) Content-specific peer feedback.
ABSTRACT
Objectives Healthcare workers face distinct occupational challenges that affect their personal health, especially during a pandemic. In this study we compare the characteristics and outcomes of Covid-19 patients who are and who are not healthcare workers (HCW). Methods We retrospectively analyzed a cohort of adult patients with known HCW status and a positive SARS-CoV-2 PCR test presenting to a large academic medical center emergency department (ED) in New York State. We routinely collect data on occupation and exposures to suspected Covid-19. The primary outcome was hospital admission. Secondary outcomes were ICU admission, need for invasive mechanical ventilation (IMV), and mortality. We compared baseline characteristics and outcomes of Covid-19 adult patients based on whether they were or were not HCW using univariable and multivariable analyses. Results From March 1 2020 through June 2020, 2,842 adult patients (mean age 53+/-19 years, 53% male) with positive SARS-CoV-2 PCR tests and known HCW status visited the ED. This included 193 (6.8%) known HCWs and 2,649 (93.2%) non-HCWs. Compared with non-HCW, HCWs were younger (43 vs 53 years, P<0.001), more likely female (118/193 vs 1211/2649, P<0.001), and more likely to have a known Covid-19 exposure (161/193 vs 946/2649, P<0.001), but had fewer comorbidities. On presentation to the ED, HCW also had lower frequencies of tachypnea (12/193 vs 426/2649, P<0.01), hypoxemia (15/193 vs 564/2649, P<0.01), bilateral opacities on imaging (38/193 vs 1189/2649, P<0.001), and lymphocytopenia (6/193 vs 532/2649, P<0.01) compared to non-HCWs. Direct discharges home from the ED were more frequent in HCW 154/193: 80% vs 1275/2649: 48% p<0.001). Hospital admissions (38/193 20% vs 1264/2694 47%, P<0.001), ICU admissions (7/193 3% vs 321/2694 12%, P<0.001), need for IMV (6/193 3% vs 321/2694 12%, P<0.001) and mortality (2/193 1% vs 219/2694 8%, P<0.01) were lower than among non-HCW. After controlling for age, sex, comorbidities, presenting vital signs and radiographic imaging, HCW were less likely to be admitted (OR 0.6, 95%CI 0.3-0.9) than non HCW. Conclusions Compared with non HCW, HCW with Covid-19 were younger, had less severe illness, and were less likely to be admitted.