The Journal of School Nursing2024, Vol. 40(2) 125–134© The Author(s) 2021Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405211047849journals.sagepub.com/home/jsn
The purpose of this study was to examine associations between caseload, social determinants, health needs, students meeting grade-level English and Math standards, and attendance. Data from the Washington State Open Data Portal and Report Card were combined with District Health Assessment data from 264 school districts. Analyses of variance and linear stepwise regression analyses were conducted. Key findings indicate significant differences in English and Math outcomes by caseloads, with higher caseload districts have lower percentages of students meeting English and Math standards, but not attendance. Caseload is a significant predictor of students meeting English and Math standards, after controlling for social determinants and district health needs. Findings point to the complexity of school nursing work and builds a foundation to guide future nursing research. Large prospective studies that allow for comprehensive measurement of structure, process and outcomes variables are needed to advance school nursing research.
Keywordsschool nurse caseload, secondary analysis, academic outcomes, school health, school nursing research
School nurses sit at the intersection of education and health, ensuring that children are supported and have access to appropriate academic opportunities (National Association of School Nurses, 2017). They play a key role in student success through the provision of direct care for children with acute and chronic conditions, care coordination, quality improvement, health leadership, and population health. However, the degree to which school nursing services, including school nurse caseload influence student educational outcomes is not well understood. Identifying the unique contribution of school nursing on educational outcomes is elusive because of the complex interactions between school nurse staffing, school environment, and social determinants of health experienced by students. A better understanding of these relationships is needed to inform decisions related to practice (staffing) and policy (distribution of resources). Therefore, the purpose of this study was to leverage an existing dataset to begin to explore associations between school nursing considerations (i.e., school nurse caseload, student characteristics, and district health care complexity) and academic outcome indicators among Washington State (WA) school districts.
Over the past 10 years, research on the practice of school nursing and outcomes has increased to demonstrate the association between school nurses and student success, defined as both good health and academic success (Leroy et al., 2017; National Association of School Nurses, 2017). Using the National Association of School Nurses 21st Century Framework for Professional School Nursing practice, Best et al. (2018) conducted an integrative review of school nurse interventions and outcomes and found that much of the existing research explored associations between interventions and health outcomes (Best et al., 2018). Among health outcomes, the majority of studies focused on care coordination. Some other health outcomes studied included: symptom days, quality of life, increased identification of students with food allergies, and immunizations (Best et al., 2018). However, the role of school nurses is to support the health of children so that they are able to learn; therefore, understanding the impact of school nurses must also include examining academic outcomes.
Research linking school nursing to academic outcomes is limited and inconsistent (Best et al., 2018; Yoder, 2020). Academic-related outcomes in the extant literature are mostly centered on absenteeism, but also include early dismissal, missed class time, and grades (Yoder, 2020). For example, Noyes et al. (2013) and Harrington et al. (2018) report decreased absenteeism among children whose asthma therapy program was supervised by a nurse in the school. Engelke’s series of studies (2008, 2011, 2014) examining school nurse case management and outcomes demonstrated an association between asthma and diabetes case management and decreased absences from school, as did Simoneau et al. (2020). However, Splett et al. (2006) did not find any differences in attendance among students in schools where school nurses participated in professional development activities to improve care to children with asthma. Similarly, Trivedi et al. (2018) reported no significant differences in school absences among students with asthma and poor medication adherence who received nurse supervised medication therapy versus non-nurse supervised therapy.
Some research has examined the association between school nurse interventions and other academic indicators such as grades (Engelke et al., 2008), academic performance as reported by the nurse (Best et al., 2021), English/Math test scores (Schroeder et al., 2023), and high school graduation rates (Yoder et al., 2021). However, the lack of standardized definitions of academic outcomes across research studies has complicated our ability to synthesize the already limited data related to school nurses and student academic outcomes. Recent studies have since examined the relationships between school nurse caseload—school nurse-to-student ratios—and grades (Best et al., 2021; Yoder et al., 2021) with the hypothesis that higher caseloads impact the ability of the school nurse to provide the comprehensive care that supports student success (Johnson et al., 2012). Best et al. (2021) reported that lower caseloads were associated with improved grades among children with asthma and diabetes, while Schroeder et al. (2023) did not find any association between caseload and academic outcomes.
The relationship between caseload and student outcomes is important to consider because of its implications on school nursing work. High caseloads usually mean that school nurses are consumed with providing direct care, with little time to engage in other activities within their scope of practice, such as case finding, surveillance, and program planning (National Association of School Nurses (NASN), 2016). For example, when caseload numbers are high, the nurse is forced to focus on emergent issues of individuals that require the technical skills of a nurse, leaving little time for higher level program interventions. Lower caseloads allow the nurse’s work to include preventative, population-level care and program management—moving from reacting to individual problems to proactively addressing the health of the school community as a whole. This more complex work requires time for aggregation and analysis of the individual data into the higher level population data to inform program management—all of which requires time to process and analyze (Denke & Winkleblack, 2020). Further, high caseloads also mean that school nurses do not have the time to develop and nurture relationships with students and create a culture of safety for vulnerable students and families (Curtis et al., 2019).
School nurse caseloads are complicated by a myriad of factors. Physical and mental health conditions of students and social determinants of health of the school community play a large role in school nurse workload. For example, students of color are more likely to be diagnosed with certain chronic health conditions (Berry et al., 2010), necessitating more nursing care in schools (Fleming, 2011) and experience more complex health problems that are rooted in structural racism. Schools and communities with high levels of poverty often have less access to resources (Rattermann et al., 2021) and school nursing care is more limited in rural areas (Willgerodt et al., 2018). However, the associations between nursing care and social determinants are mixed. Gratz et al. (2023) report a negative relationship between students of color and caseload where districts with higher numbers of students of color had lower caseloads among nurses, while others report that non-White students are more likely to be enrolled in schools with fewer services (Tiu et al., 2019). Similarly, rural children have less access to a school nurse compared to urban youth (Willgerodt et al., 2018). Yoder et al. (2021) examined ratios of low-income students-to-school nurses and found that the ratios were a negative predictor of being on track to graduate and graduation rates (higher ratios associated with lower numbers of students on track to graduate and lower graduation rates), but total number of students-to-nurse ratios were not predictive of academic outcomes. The minimal data and inconsistent findings underscore the need for continued large-scale empirically based examinations of school nurse impacts on student academic outcomes.
This study was guided by the 3S (Student–School Nurse–School Community) Model (Wolfe et al., 2019), an adaptation of Donabedian’s structure–process–outcomes health care quality model (Donabedian, 2005). In response to the NASN’s strategic research priorities (Bergren, 2021), we conducted a secondary analysis to explore the association between structure-related variables and academic outcomes, with structure variables conceptualized by social determinants of health, health needs, and school nurse caseload. Three outcome variables were considered: students meeting grade-level English and Math standards, and attendance.
This study was reviewed by the University of Washington and deemed exempt from human subjects review. Data for this cross-sectional secondary analysis were drawn from three large datasets: the WA State District Health Assessment (DHA), the WA Open Data Portal, and WA State Report Card. The DHA are aggregated reports of district level health data that are collected at the end of each academic year by the Washington State School Nurse Corps Administrators (SCNA)—school nurse administrators from each of the nine educational service areas across WA state (Fast et al., 2013). The SCNA provide professional and technical support to the school nurses and districts within their educational service area and oversee the collection of the DHA. DHA data include a range of school nurse characteristics and services including demographics, staffing mix, and health conditions of students served. The Washington Open Data Portal is a repository of public use data on student and school characteristics including student demographics and school enrollment. State academic performance benchmarks for each school district are publicly available through the Washington State Report Card (https://washingtonstatereportcard.ospi.k12.wa.us/).
The analytic sample for this study included all Washington State school districts that provided district health assessment data for the school year 2018–2019 (N = 264 districts).
Data on district demographics, attendance, and state testing results were imported into an SPSS file, together with the DHA data from 264 districts. Missing data from the DHA forms were examined, and follow-up queries were made to the SCNA to determine if the data was available. If additional data was provided from the SCNA, updates were made to the database. Thirty-one districts did not submit a DHA form.
Structured Variables. Structure-related variables included district demographics, the health complexity of the districts, and school nurse caseload. District-level percentages were calculated for each of the variables to account for size.
Urbanicity. Districts were classified as urban/rural using classification based on the National Center for Education Statistics (NCES) coding (Geverdt, 2015). Specifically, locales where a school is situated are classified as urban, suburban, town, or rural. For our study, urban or suburban schools were merged and designated as urban, and town or rural schools were merged and designated as rural, to create a binary variable for analysis. Districts with 90% or more of schools falling into one of the two classifications (urban/rural) were classified as such. The remaining school’s urbanicity data were given to the SCNA, who were asked to classify the districts as urban or rural, based on their knowledge of the districts in their service area.
Income. Washington State identifies schools as low-income if 40% or more students in the school are enrolled in the Federal Free and Reduced Lunch Program (FRLP). Thus, districts were classified as low-income if more than 40% of schools in the districts were enrolled in FRLP.
Homelessness. Percentages of students in each district who are identified with unstable housing.
Ethnicity/Race. Percentages of students who identify as American Indian/Alaskan Native, Asian, Black/African American, Hispanic/Latino of any race, Native Hawaiian/ Other Pacific Islander, two or more races, and White.
Health Complexity. A health complexity variable was calculated to allow one variable to be included in the analysis that was a representation of potential school nurse needs in each district. This was computed by the number of students in each of the health acuity categories defined by Washington State (Washington State Nursing Care Quality Assurance Commission & Instruction, 2000), weighted by complexity of each category; nursing-dependent (Level A), medically fragile (Level B), medically complex (Level C) and schools with health concerns (Level D). A composite score was computed with those in the higher complex level (Level A) receiving 4 times the value reported, the next highest (Level B) receiving 3 times the value reported, and so forth. The total health complexity per district was calculated as: 4*Level A value + 3* Level B value + 2*Level C value + 1*Level D Value.
Disabilities. This indicates percentages of students for whom a Section 504 plan exists.
Caseload. School nurse full-time equivalents (FTE) were calculated using 40 h per week constituting one FTE. Caseloads were then calculated using school nurse FTE-to-student ratios.
Outcome Variables. Outcome measures included attendance measured by percentage of students in each district with fewer than two absences/month per WA state’s definition of attendance, percentage of students in each district meeting grade-level English standards, and percentage of students in each district meeting grade-level Math standards.
Analyses were conducted to examine the relationship between school nurse caseload, students who met gradelevel standards in the state English and Math assessments, and attendance. Descriptive statistics on the structure and outcome variables of interest were assessed and summarized for normal distributions. Correlation tables were reviewed to ensure that no variables had a collinear relationship, which could alter the regression analyses.
To conduct analyses examining the relationship between social determinants of health (SDOH), health needs, caseload and outcome variables, several categorical variables were created. First, a “SDOH” variable was created to identify districts with high, medium, and low levels of social determinants. Social determinants were based on the percentage of students who were identified as homeless, non-White and low income in each district. The median percentages of these social determinant items were used to determine if a district was high or low for each determinant. The median value for homelessness was 3%, the median value for students who were non-White was 30%, and any district with >40% of the students who qualified for the FRLP was considered a low-income district. The three levels of SDOH were then computed based on how many of these three social determinants applied to a district. If a district had >3% of students who were homeless and had >40% students who were non-White, and was considered a low-income district, the district was labeled as high SDOH. If only two of these social determinants existed for a district, they were considered a medium SDOH district. If only one of these social determinants existed for a district, it was labeled as a low SDOH district.
High/low health needs districts were represented using the district heath complexity and disability percentages. If the district health complexity composite score was ≥236 (the median) and the mean percentage of students with 504 plans was ≥ 3.24% (median percentage of entire sample), the district was designated as a high health needs district. If the district health complexity composite score was <236 and/or the percentage of students with a 504 plan was <3.24%, then the district was designated as a low health needs district.
The NASN and American Academy of Pediatrics (APP) recommend that a registered school nurse (RN) be present in every school (AAP Council on School Health, 2016; National Association of School Nurses, 2019). For this reason, analyses exploring the caseloads with outcomes utilized the RN FTE-to-student ratio. Three groups of caseloads (low, medium and high) were calculated, by dividing the 264 districts into three equal groups of school nurse-to-student-ratios. The resultant groupings were less than 1,030 students, 1,030–1,615, and >1,615 students per nurse. Analyses of variance (ANOVA) were conducted to examine how outcomes differed by urban/rural status, SDOH, health needs, and caseload.
Linear stepwise regression analyses were performed to determine the influence of caseload on each of the outcome variables. To account for district size, percentages for each of the predictor variables were used. For race/ethnicity, the percentage of non-White students was calculated for each district. Social determinants (% homeless, non-White, % low-income) and urbanicity were entered into the regression equation as the first block or set of variables, followed by district health needs (district health complexity, % students with 504 plans). The final step in the regression analysis was to enter caseload to determine if this improved the model, and was positively related to the outcomes. Interactions effects were reviewed, but were nonsignificant and so were not included in the final model.
The final dataset consisted of 2018–2019 health assessment data from 264 of the 295 school districts in Washington state, which accounted for 1,757 schools and 914,889 students statewide (Table 1). The total number of FTE of all school nurses reported in the data was 991, with 766 of them being RN FTEs. The remaining FTEs (n = 225) were licensed practical nurses (LPN). The sample comprised 159 rural districts with a total of 75.9 RN FTEs, compared to 105 urban school districts with 690 RN FTEs. The statewide caseload was 1194.29 students per RN, with a mean caseload per district of 1,650. Sample characteristics are displayed in Tables 1 and 2.
In terms of academic outcomes, significant differences were found across urbanicity with rural districts having significantly lower mean percentages of students compared to urban districts meeting English (mean = 52.87 and 57.87, respectively, p = .003) and Math standards (means = 39.23 and 46.76 respectively, p < .001), and attendance (means = 81.18 and 83.07 respectively, p = .039) (Table 3). Districts categorized as having high SDOH had lower percentages of students meeting English and Math standards and lower attendance rates compared to medium and low SDOH districts (overall p < .001 for all outcome measures). Districts that had high health needs had significantly higher percentages of students meeting Math standards (p = .004) and higher attendance rates (p = .021). Significant differences were also noted in English (p = .001) and Math (p = .014) outcomes by caseloads, with higher caseload districts having lower percentages of students meeting English and Math standards, but not with attendance (p = .503).
Stepwise regression was used to determine which health needs and social determinants affected the outcome of interest, with the RN caseload entered as a second step predictor. Final regression models demonstrate that across all districts, after adjusting for social determinants and health needs, caseload significantly predicts the percentages of students meeting both English and Math standards (β = −.123, p = .007, β = −.174, p = <.001, respectively), suggesting that a district with a lower school nurse caseloads will have more students meeting the standards for English and Math standardized tests. Caseload did not influence attendance. See Table 4(a–c).
This study explored relationships between social determinants, health needs, school nurse caseload, and academic outcomes. Previous research on the impact of school nursing has mostly examined the associations between school nursing practice and health. Those that examined connections between school nurses and academic outcomes were either centered on specific interventions or used attendance as the main academic outcome. As one of the few studies examining the influence of caseload on academic outcomes, this study adds to the body of literature by examining caseloads in the context of social influences of health and district health needs.
Our data indicate differences in school nurse caseload by urbanicity, with nurses in rural areas having higher total caseloads than in urban districts. Our data also indicate that caseloads are higher in high SDOH, and high health issue school districts. These data align with those of Gratz et al. (2023) who, using a different methodology, found fewer school nurses in rural and school districts in Washington State. Gratz et al. (2023) also found that districts with higher numbers of students of color had lower school nurse caseloads. However, using an SDOH variable that considers race, income, and homelessness, our data indicate that total caseloads in districts with higher levels of SDOH are higher, which aligns with other work that suggests that lowincome, rural schools are less resourced (Owens et al., 2016; Rattermann et al., 2021) and have less access to nursing care (Gratz et al., 2023). Staffing decisions are made in consideration of health needs and presumably districts with high health needs would have lower caseloads to account for the nursing care needed, but our findings for Washington State suggest that caseloads are not markedly different between high and low health needs districts (Table 2). Additionally, while other research report on total caseload, we calculated mean caseloads for each strata within variable which may serve as a better indicator of school nursing work. Mean caseloads by urbanicity, SDOH, or health needs are not significantly different. These results highlight not only the complexities of understanding the interrelationships between race, income, homelessness, and school nursing practice, but also the need for close examination of how data are measured and analyzed, particularly when using district-level data. Moreover, in our study, we examined caseload for RNs, which does not account for LPN nurses in schools. Given the variation in educational preparation of school nurses (Willgerodt et al., 2018), it is possible that districts with high SDOH or health needs may be supplementing RNs with LPN or health aides.
Differences in academic outcomes by urbanity and SDOH noted in this study are consistent with existing literature that indicates that students of color tend to underperform academically compared to their White peers (de Brey et al., 2019). Unexpectedly, we find that districts with higher levels of health needs have higher percentages of students meeting English and Math standards and better attendance records. This is perplexing given that our data also indicate that districts with higher levels of health needs do not have markedly different caseloads from districts with lower levels of health needs. Research studies that report similar results (higher health need districts have better academic outcomes) have posited that these schools and districts have more school nurses (and lower caseloads) translating to more support to ensure success (Yoder et al., 2021). However, our study does not support this hypothesis as our data do not indicate any significant differences in either total or mean caseloads. The existing research data remain mixed, and more research geared toward understanding these associations is warranted.
Our data indicate that districts with lower caseloads were associated with higher percentages of students meeting grade-level expectations. This could mean that with lower caseloads, school nurses have the time to develop supportive relationships with students, and facilitate positive learning environments that promote overall health and well-being. Data from other studies show that students perform better academically when in positively perceived learning environments, report higher levels of school connectedness and have access to health care (Sisselman et al., 2012; Strolin-Goltzman et al., 2014). School nurses may serve as an important contributor to these outcomes. Our findings highlight the importance of not simply ensuring that these districts have access to school nurses but that their caseload is being considered.
After controlling for social determinants and health needs, our data indicate that lower caseload continues to predict better academic outcomes. The small amount of explained variance that caseload contributed to the overall model of academic outcomes necessitates a more comprehensive approach to understanding student academic success. High levels of student SDOH or health needs are often the rationale for increased access to a registered nurse at school but our data suggest that school nurses may play an important role in contributing to a healthy school environment irrespective of social determinants and health needs, and contribute to schools meeting their academic goals. While the amount of explained variance is small (Table 4), caseload remains a significant predictor that is adaptable. In other words, one cannot control the social determinants or level of health needs within districts, but one has the control over school nurse staffing and caseloads. Findings from this study add to the growing body of research and expand results from previous research by using a large state-wide dataset and examining school, student, and school nurse structural variables in relation to academic outcomes.
School nursing services research is particularly challenging given the complexity of these structural factors, as well as intersecting health and educational processes (e.g., school nurse interventions and teaching modalities) that also influence student health and learning outcomes. Specifying the unique contributions of school nurses to objective measures such as academic outcomes is difficult without taking into consideration social determinants and larger school and community contexts that drive academic success. The role of school nurses in improving student academic outcomes must be situated within such a broad framework. While secondary analyses of large datasets are valuable in providing foundational information, they are limited by variability in existing data sources, intended more for tracking student outcomes and not necessarily for research purposes. The NASN National School Health Dataset: Every Student Counts! begins to address the lack of standardization in school health data collection by identifying and defining standardized variables to support uniform data collection across multiple entities and facilitate strong secondary analyses (National Association of School Nurses, 2018). The National School Health Dataset will need to continue to grow to include contextual variables and large prospective studies are needed to fully understand the complexity of the structures and processes and to articulate what school nurses are able to contribute to the school climate with lower caseloads and impact student health and educational outcomes.
Our study findings should be interpreted with caution as several limitations in the data exist. Our data are drawn from district reports of school nursing services. As with any self-reported data, we are dependent on accurate reporting of that data. Collection of school nursing data has been challenging due to lack of standardization in terminology and definitions, and regional differences in which factors influence how staffing decisions are made, all of which impact the fidelity of the data. The lack of standardization applies to difference in academic outcomes as well. For example, while NASN defines high absenteeism as >3 absences per month, Washington State standards for attendance is set at <2 absences per month and our data did not allow us to adjust for these metrics. These types of challenges are common with secondary analyses, but are accentuated in school nursing research where this lack of consistency has limited the ability to accurately interpret research findings.
In addition, we used district-level aggregated data that were cross-sectional, which masks the high variability within districts and nuances in the relationships between social determinants, health needs, school nursing services, and school demographics such as school size or class sizes. Moreover, we created composite variables from the available data to obtain an indication of the complexity of health needs within districts. The variables used in constructing the composite variable may not fully or accurately reflect the health needs of a district. Last, this secondary analysis required the use of caseload as a means to examine the impact of school nurses on academic outcomes and it is well known that caseload is not indicative of workload. In fact, NASN has moved away from recommending a standard caseload for school nursing, knowing that caseload numbers alone do not capture the complexities of school nursing workload, and that staffing decisions need to consider a plethora of other factors (Combe et al., 2015). Despite these limitations, this study provides an indication that school nurse caseload, and potentially practice processes, do influence student academic outcomes even after adjusting for social determinants and health needs.
Study data have implications for school nursing practice and research. Our research has documented a significant association between caseload and academic outcomes. Practicing school nurses may utilize these findings to help school administrators and policy makers understand the importance of school nurses in supporting a healthy school environment that goes beyond providing care to those students considered at risk because of social or health factors. Our data also help illuminate the challenges of articulating the role and impact of school nurses on educational success. School nurses may aid in these efforts by fully engaging in data collection efforts as we continue to grow the research in this area and importantly, advocate for school policies that support school nurse-related data collection.
Findings will inform future school nursing research. This study provides a robust foundation to begin considering not only the nuances between school, school nurse and student factors identified in the 3S Model in impacting academic success, but also to conceptualize future school nursing research comprehensively to include school nurses, teachers, and counselors and how they interact in supporting student success. The Whole School, Whole Community, Whole Child (WSCC) Model, supported by NASN, is grounded in a perspective that emphasizes collaboration and coordination across multiple sectors within the school and community to promote a culture of health (Lewallen, Hunt, Potts-Datema, Zaza, Giles, 2015). Further research can build on our findings to contribute to the empirical data needed in relation to the WSCC model (Willgerodt, Walsh, & Maloy, 2021). Importantly, large prospective studies are needed that would allow for comprehensive measurement of structure, process, and outcome variables that are meaningfully reflective of school nursing work.
Identifying caseload as a significant independent predictor of student academic outcomes, after controlling for social determinants and health needs, allows us to understand that school nurses contribute to the overall school environment and student academic outcomes. With this understanding, school nurses may be better positioned to articulate the importance of caseload in supporting positive school environments, and school nursing research may use this as a foundation to extend our understanding of the complexity of what students need to be healthy and successful.
The authors gratefully acknowledge the School Nurse Corps Administrators of Washington State for their support and partnership, and Dr. Kathleen Johnson for her editorial review and feedback in preparing this manuscript.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors disclosed receipt of the following financial support for the research and/or authorship of this article: This study was funded by the National Association of School Nurses.
Mayumi A. Willgerodt https://orcid.org/0000-0002-9874-3739
Kristin Griffith https://orcid.org/0000-0002-6078-0622
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University of Washington, Seattle, WA, USA
Corresponding Author:Mayumi A. Willgerodt Associate Professor, University of Washington, Box 357262, Seattle, WA 98195, USAEmail: mayumiw@uw.edu
