International, National, and Statewide Testing Programs

Keyword(s):  
2020 ◽  
Author(s):  
Thomas Duszynski ◽  
William Fadel ◽  
Kara Wools-Kaloustian ◽  
Brian Dixon ◽  
Constantin Yiannoutsos ◽  
...  

Abstract Background Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or tobacco use is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. Methods Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n= 8,214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in April or June of 2020. Before testing, participants provided demographic information and self-reported health status and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco with SARS-CoV-2 positivity on either PCR or antibody tests.Results Compared to people with self-identified “excellent” or very good health status, those reporting “good” or “fair” health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. Conclusions In a statewide, community-based population drawn for seroprevalence studies, we find that overall health status is associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.


2020 ◽  
Vol 49 (5) ◽  
pp. 335-349
Author(s):  
Allison Atteberry ◽  
Daniel Mangan

Papay (2011) noticed that teacher value-added measures (VAMs) from a statistical model using the most common pre/post testing timeframe–current-year spring relative to previous spring (SS)–are essentially unrelated to those same teachers’ VAMs when instead using next-fall relative to current-fall (FF). This is concerning since this choice–made solely as an artifact of the timing of statewide testing–produces an entirely different ranking of teachers’ effectiveness. Since subsequent studies (grades K/1) have not replicated these findings, we revisit and extend Papay’s analyses in another Grade 3–8 setting. We find similarly low correlations (.13–.15) that persist across value-added specifications. We delineate and apply a literature-based framework for considering the role of summer learning loss in producing these low correlations.


1997 ◽  
Vol 11 (4) ◽  
pp. 227-236 ◽  
Author(s):  
TINA T. CHARBONNEAU ◽  
NANCY A. WADE ◽  
LEONARD WEINER ◽  
JACKSON OMENE ◽  
LISA FRENKEL ◽  
...  

1996 ◽  
Vol 29 (1) ◽  
pp. 49-67 ◽  
Author(s):  
Mark D. Shermis ◽  
Paul M. Stemmer ◽  
Patrick M. Webb

2012 ◽  
Vol 48 (3) ◽  
pp. 159-166 ◽  
Author(s):  
Valerie L. Mazzotti ◽  
Dawn R. Rowe ◽  
David W. Test

Factors such as the standards-based education movement, mandated participation in statewide testing, and inclusion have forced an increased focus on improving outcomes for students with disabilities. There are many determinants of postschool success for students with disabilities; however, teachers primarily have control over only one, teaching practices and programs. As a result, it is important that teachers choose and implement practices that have proven successful for secondary students with disabilities. This article guides teachers through the process of navigating the evidence-based practice maze to identify evidence-based practices and programs for secondary students with disabilities. Particularly, it addresses the need to (a) follow a research-based framework (i.e., Kohler’s Taxonomy), (b) use practices with the best available research evidence to support effectiveness, and (c) use data-based decision making to guide use of evidence-based practices.


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