National School Health Data Set: Every Student Counts! Data Submitted for 2019-2020 School Year

2021 ◽  
Vol 36 (2) ◽  
pp. 85-85
2018 ◽  
Vol 33 (5) ◽  
pp. 291-294 ◽  
Author(s):  
Erin D. Maughan ◽  
Kathleen H. Johnson ◽  
Martha Dewey Bergren

The National Association of School Nurses (NASN) is launching a new data initiative: National School Health Data Set: Every Student Counts! This article describes the vision of the initiative, as well as what school nurses can do to advance a data-driven school health culture. This is the first article in a data and school nursing series for the 2018-2019 school year. For more information on NASN’s initiative and to learn how school nurses can join the data revolution, go to http://nasn.org/everystudentcounts


2020 ◽  
Vol 35 (2) ◽  
pp. 89-90
Author(s):  
Erin D. Maughan

The NASN launched a new data initiative in 2018 called: The National School Health Data Set: Every Student Counts! The initiative includes three distinct foci or prongs. This article reports on the progress of states participating in Every Student Counts! For more information on NASN’s initiative and to learn how school nurses can join the data revolution, go to http://nasn.org/everystudentcounts .


2020 ◽  
Vol 36 (1) ◽  
pp. 29-31
Author(s):  
Erin D. Maughan ◽  
Martha Dewey Bergren ◽  
Kathleen Johnson

The National Association of School Nurses’ (NASN’s) data initiative The National School Health Data Set: Every Student Counts! (Every Student Counts!) is getting a new platform! This article reviews what Every Student Counts! is and shares some of the new features of the platform. For more information on NASN’s initiative and to learn how school nurses can join the data revolution go to http://nasn.org/everystudentcounts


2018 ◽  
Vol 33 (6) ◽  
pp. 359-363 ◽  
Author(s):  
Kathleen H. Johnson ◽  
Lynne P. Meadows ◽  
Martha Dewey Bergren ◽  
Erin D. Maughan

The National Association of School Nurses (NASN) has launched the National School Health Data Set: Every Student Counts! Building on the success of previous school health data collection, this article describes the steps that the Georgia Association of School Nurses takes to promote the collection of data to support the health of Georgia’s school-age children. Building a team, engaging stakeholders, mapping a plan of action, and developing the message are described as ways to build the capacity for data collection. Other states and NASN state affiliates may learn from the ideas presented here.


2020 ◽  
Vol 35 (3) ◽  
pp. 140-142
Author(s):  
Martha Dewey Bergren ◽  
Erin D. Maughan

Nurses in the 21st-century are expected to be data and information literate and proficient in data management. Nurses graduating from baccalaureate programs must be able to use computers and information systems and apply data and evidence to inform practice. Those competencies are also essential for the entire nursing workforce. That puts the onus on school nurses, school nurse supervisors, school districts, and state affiliates to take responsibility for comprehensive data and information literacy professional development. Fortunately, the National Association of School Nurses (NASN) has anticipated the needs of the membership. NASN included data and information capacity building as a part of The National School Health Data Set: Every Student Counts!, a national standardized data set and data collection initiative.


2016 ◽  
Vol 32 (1) ◽  
pp. 39-41 ◽  
Author(s):  
Martha Dewey Bergren ◽  
Erin D. Maughan ◽  
Kathleen H. Johnson ◽  
Linda C. Wolfe ◽  
H. Estelle S. Watts ◽  
...  

There are many stakeholders for school health data. Each one has a stake in the quality and accuracy of the health data collected and reported in schools. The joint NASN and NASSNC national school nurse data set initiative, Step Up & Be Counted!, heightens the need to assure accurate and precise data. The use of a standardized terminology allows the data on school health care delivered in local schools to be aggregated for use at the local, state, and national levels. The use of uniform terminology demands that data elements be defined and that accurate and reliable data are entered into the database. Barriers to accurate data are misunderstanding of accurate data needs, student caseloads that exceed the national recommendations, lack of electronic student health records, and electronic student health records that do not collect the indicators using the standardized terminology or definitions. The quality of the data that school nurses report and share has an impact at the personal, district, state, and national levels and influences the confidence and quality of the decisions made using that data.


2020 ◽  
Vol 63 (6) ◽  
pp. 1947-1957
Author(s):  
Alexandra Hollo ◽  
Johanna L. Staubitz ◽  
Jason C. Chow

Purpose Although sampling teachers' child-directed speech in school settings is needed to understand the influence of linguistic input on child outcomes, empirical guidance for measurement procedures needed to obtain representative samples is lacking. To optimize resources needed to transcribe, code, and analyze classroom samples, this exploratory study assessed the minimum number and duration of samples needed for a reliable analysis of conventional and researcher-developed measures of teacher talk in elementary classrooms. Method This study applied fully crossed, Person (teacher) × Session (samples obtained on 3 separate occasions) generalizability studies to analyze an extant data set of three 10-min language samples provided by 28 general and special education teachers recorded during large-group instruction across the school year. Subsequently, a series of decision studies estimated of the number and duration of sessions needed to obtain the criterion g coefficient ( g > .70). Results The most stable variables were total number of words and mazes, requiring only a single 10-min sample, two 6-min samples, or three 3-min samples to reach criterion. No measured variables related to content or complexity were adequately stable regardless of number and duration of samples. Conclusions Generalizability studies confirmed that a large proportion of variance was attributable to individuals rather than the sampling occasion when analyzing the amount and fluency of spontaneous teacher talk. In general, conventionally reported outcomes were more stable than researcher-developed codes, which suggests some categories of teacher talk are more context dependent than others and thus require more intensive data collection to measure reliably.


2010 ◽  
Vol 27 (2) ◽  
pp. 102-110 ◽  
Author(s):  
Kathleen Hoy Johnson ◽  
Martha Dewey Bergren

2015 ◽  
Vol 54 (1) ◽  
pp. 3-34 ◽  
Author(s):  
Michael A. Gottfried

Although educational policy makers uphold that chronic absenteeism (missing 10% or more of the school year) is damaging to students’ schooling outcomes, there is little empirical research to match. This study considers the role of spillover effects of chronic absenteeism on classmates’ achievement. It does so by utilizing a large-scale administrative urban district data set of elementary schoolchildren—a sample of students where the rates of chronic absenteeism are expected to be higher compared with the national average. The results show that students suffer academically from having chronically absent classmates—as exhibited across both reading and math testing outcomes. Chronic absenteeism not only had a damaging effect on those individuals missing excessive school days but also has the potential to reduce outcomes for others in the same educational setting.


2020 ◽  
pp. 019874292096135
Author(s):  
Nicholas A. Gage ◽  
Antonis Katsiyannis ◽  
Kelly M. Carrero ◽  
Rhonda Miller ◽  
Danielle Pico

The Latinx population is the largest group of racially and ethnically diverse students in the United States. Although disproportionality in school discipline has been documented for Latinx students, findings related to such disparities have been inconsistent. We examined disciplinary exclusion practices involving students with and without disabilities who are Latinx across the United States using risk ratios (RR) and weighted mixed-effect models. We leveraged data from the Civil Rights Data Collection (CRDC) data set for the 2015 to 2016 academic school year, which included data from more than 94,000 schools. The CRDC is collected by the U.S. Department of Education’s Office of Civil Rights every 2 years. All U.S. public schools are required to submit data to the CRDC. Results suggest that Latinx students with and without disabilities were statistically significantly more likely to receive exclusionary discipline than White students, but less likely than Black students. Implications for research and practice are provided.


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