The Journal of School Nursing2023, Vol. 39(4) 305–312© The Author(s) 2021Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405211012957journals.sagepub.com/home/jsn
This study assessed associations between school nurse workload and student health and academic outcomes. We hypothesized that lower school nurse workload would be associated with better student outcomes, with associations being greater for members of groups who experience health disparities. Our methods entailed secondary analysis of data for New York City school students in kindergarten through 12th grade during 2015–2016 (N = 1,080,923), using multilevel multivariate regression as the analytic approach. Results demonstrated lower school nurse workload was associated with better outcomes for student participation in asthma education but not chronic absenteeism, early dismissals, health office visits, immunization compliance, academic achievement, or overweight/obesity. Our findings suggest school nurses may influence proximal outcomes, such as participation in disease-related education, more easily than downstream outcomes, such as absenteeism or obesity. While contrary to our hypotheses, results align with the fact that school nurses deliver community-based, population health–focused care that is inherently complex, multilevel, and directly impacted by social determinants of health. Future research should explore school nurses’ perspectives on what factors influence their workload and how they can best impact student outcomes.
Keywordsschool nursing, nurse workload, nurse staffing, student health, school health
School nurses contribute directly to the health and wellbeing of children, families, and communities (National Association of School Nurses, 2018). Recent literature highlights positive impacts of school nurses on school-level outcomes such as improvements in student immunization rates and completion of health screenings and referrals (Baisch et al., 2011; Best et al., 2018; Kocoglu & Emiroglu, 2017). When school nurses implement comprehensive care, student absenteeism is lowered and grade point average increases (Kocoglu & Emiroglu, 2017). School nursing services are cost-beneficial (Wang et al., 2014). The presence of a school nurse can also save school staff time; one study estimated principals spent 57 and teachers 20 fewer min dealing with health issues when a school nurse is available, leading to hundreds of thousands of dollars of potential savings when a school hires a school nurse (Baisch et al., 2011). However, there exists a dearth of research examining how school nurse workload relates to student outcomes (Yoder, 2020) in contrast with extensive evidence on beneficial effects of appropriate nurse workload on patient outcomes in hospital settings (Aiken et al., 2003; Aiken et al., 2002; Aiken et al., 2014; Rafferty et al., 2007). Evidence about the association between school nurse workload and student outcomes can inform school administrators, school nurses, school nurse leaders, and policy makers who make decisions related to school nurse staffing and funding. Importantly, such evidence would be directly relevant to the well-being of children in schools across the world.
The purpose of this study was to assess the association of school nurse workload with student health and academic outcomes. More specifically, our primary aim was to assess the association between school nurse workload and four school nurse–sensitive indicators (primary student outcomes); our second aim was to assess the association between school nurse workload and participation in asthma education, English and math academic achievement, and overweight/obesity (secondary student outcomes). An exploratory aim assessed whether associations between nurse workload and nurse-sensitive indicators differed for students from populations who experience health inequities (students who are members of racial/ethnic minority groups, live in low-income households, or live in high-poverty neighborhoods). We hypothesized that lower school nurse workload would be associated with better student outcomes, with associations being greater for members of groups who experience health inequities.
This study was a secondary analysis of existing cross-sectional data. Study reporting is consistent with Strengthening the Reporting of Observational Studies in Epidemiology guidelines (von Elm et al., 2007).
The theoretical framework for this study is the Student– School Nurse–School Community (3S) Model (Wolfe et al., 2019). The 3S Model provides a guiding framework for considering how existing data sources can be applied to demonstrate outcomes related to school nursing intervention (Wolfe et al., 2019). The 3S Model visually represents how school nurse, student, and school community structures impact school nurse, student, and school community outcomes via the process of school nursing interventions; in the Model’s context, structures are the factors that influence the ability to deliver quality care, processes are the actions taken to improve or maintain health, and outcomes are the results of nursing intervention. In this study, we assessed the association of one school nurse–related structure (workload) with multiple student outcomes. How each study measure aligns with the 3S Model is detailed in Table 1.
Temple University Institutional Review Board approved the study (protocol number 25797) and confirmed waiver of consent and assent.
All students attending New York City (NYC) schools during the 2015–2016 school year were eligible for inclusion. The setting was NYC public schools during the school year 2015–2016.
Multiple existing data sources were used in this study. Student records provided demographics and academic achievement data, which are collected by school staff via routine school procedures. FitnessGram records provided overweight/obesity data, which are collected by physical education teachers during annual fitness assessments (NYC Department of Education, 2019). The Automated Student Health Record (the NYC Office of School Health’s electronic medical record) provided health outcome data, which are collected by school nurses during routine clinical care. Administrative records provided nurse workload per school, which is collected by school nursing administrators as part of routine school nurse staffing documentation. United States Census 2015 American Community Survey provided the percentage of individuals below the federal poverty level in the student’s home Census tract (United States Census Bureau, 2021), which is collected via Census’ annual survey of 3.5 million American households. The poverty data used in this study are from the American Community Survey’s 5-year estimates, which minimize statistical error for small geographic areas (United States Census Bureau, 2020). Data sources were linked to one another by a unique student identifier, school, and Census tract.
See Table 1 for a list of study measures including each measure’s operational definition. The independent variable was nurse workload, defined using an existing NYC school system metric that incorporates nurse-to-student ratio and the number of children with diabetes, asthma, allergies, and other medical care needs. The metric was developed by NYC Office of School Health for internal use in order to measure nurse workload and plan for staffing. The workload metric is operationalized using “points” (e.g., 20 workload points indicates higher nurse workload than 3 workload points). Because there is no a priori point cutoff for workload categories, for this study, nurse workload was split into three equal-sized groups that were categorized as low, moderate, and high workload.
The dependent variables were student outcomes, with a separate model run for each outcome. The four primary student outcomes were nurse-sensitive indicators: chronic absenteeism (yes/no), early dismissals (number of), health office visits (number of), and immunization compliance (yes/no). The four secondary student outcomes were participation in asthma education (completion of Open Airways Asthma self-management program [yes/no]), English academic achievement (New York State English testing score), math academic achievement (New York State math testing score), and overweight/obesity (yes/no).
The covariates were student age in years, student sex (male or female), student race/ethnicity (Asian/Pacific Islander, non-Hispanic Black/African American, Hispanic, Native American/American Indian, non-Hispanic White, or Other), student household poverty (receipt of free/reduced lunch or public assistance [yes/no]), student Englishlanguage learner status (yes/no), and school poverty (percentage of students per school with household poverty). (English-language learners are students who are unable to communicate fluently or learn in English and require specialized instruction in both English and course content.) Subgroup analyses were done by race/ethnicity, household poverty level, and neighborhood poverty level. For subgroup analyses, race/ethnicity and household poverty were categorized as described above, and neighborhood poverty was categorized using groupings identified by the Public Health Disparities Geocoding Project and tailored for NYC (<10%, 10%–<20%, 20%–<30%, and>30%; Toprani & Hadler, 2013).
Demographic characteristics were assessed using standard descriptive statistics, with measures of central tendency and variation used for continuous variables and frequencies and percentages for categorical variables. Multilevel multivariate regression was used to assess the association with nurse workload (independent variable) with each dependent variable, controlling for covariates and accounting for clustering at the school level. Analyses were conducted using SAS (2019) Version 9.4. Exploratory subgroup analyses were conducted by race/ethnicity and household and neighborhood poverty levels.
Sample demographics are presented in Table 2. Of the N = 1,080,923 students, the mean age was 11.37 ± 4.05 years, and 49% were female. Fifteen percent were English-language learners. Students race/ethnicity was Black/African American (41%), Hispanic (26%), Asian/Pacific Islander (16%), non-Hispanic White (15%), Multiracial/Other (1%), and Native American/American Indian (1%). Students had an average of 12.59 ± 18.27 days absent, and 21% were chronically absent. They had a mean of 1.61 ± 3.10 health office visits andlessthanone(0.25+ 0.71) early dismissal during the year. Most were immunization compliant (92%) and had not completed Open Airways asthma self-management program (84%). Approximately one third (36%) had overweight or obesity.
Results of multivariate analyses are presented in Table 3. Lower nurse workload was associated with better odds of receiving asthma education. Lower nurse workload was not associated with better outcomes for nurse-sensitive indicators, English achievement, math achievement, or overweight/obesity. Exploratory subgroup analyses demonstrated similar associations (data not shown; see Supplementary Files).
Our study assessed the association of school nurse workload with student health and academic outcomes, with the goal of building the evidence about school nurses’ ability to support student well-being and learning success. We found that lower nurse workload was favorably associated with student participation in asthma education but not other student outcomes measured in this study. When considering our results within the broader context of existing research, theory, and clinical experience, we propose two potential explanations for our results: (1) our nurse workload measure, which focused on clinical tasks but not social determinants of health, did not capture many of the complex factors that influence workload and (2) schools nurses may be able to influence proximal health outcomes, such as the provision of disease-related education, more easily than complex downstream outcomes such as absenteeism or obesity.
Social determinants of health can directly impact school nursing workload and student outcomes (Schroeder, Malone, McCabe, & Lipman, 2018). The National Association of School Nurses position statement on “School Nurse Workload: Staffing for Safe Care Nurses” highlights the importance of considering social needs, school context, and health inequities when considering nurse staffing (National Association of School Nurses, 2020). Prior research has shown that social determinants, such as poverty, can be key drivers of school nursing service utilization (Fleming, 2011). Many school nurses can describe tasks that are influenced by social determinants of health and contribute to their workload but are not direct clinical care; for example, in highpoverty districts where many parents may work multiple jobs or have poor health literacy, school nurses may devote substantial time to communicating with families about students’ health. School nurses in high-need districts may spend a great deal of time providing social supports to families and connecting students with resources. These important and time-intensive nursing interventions are not considered in most workload measures, which focus on counts of clinical tasks or nurse-to-student ratios. Given school nurses’ role as population health leaders who deliver care in the community setting (National Association of School Nurses, 2018), social determinants of health (and not only clinical indicators) are relevant to measures of workload. For example, factors such as school poverty level, percentage of families with English-language communication challenges, or percentage of students experiencing family disruption could be considered when planning school nurse staffing. Such factors would merit consideration of all school settings regardless of school demographics, neighborhood poverty level, or geographic location. While the inclusion of social determinants in workload measures has not yet been done widely in school nursing, our results highlight the importance of accurately capturing the complex, diverse work done by school nurses.
Our study and its implications should be considered within the context of prior work on school nurse workload. Existing research has often relied on nurse-to-student ratio to assess workload (Yoder, 2020), a measure that is not sufficiently comprehensive (National Association of School Nurses, 2020). While past efforts to define school nurse workload exist, many have not been widely utilized nor psychometrically tested (Jameson et al., 2020). Lack of clarity about and measures for school nurse workload may be due to the complexity of school nurses’ role, which includes diverse responsibilities crossing health promotion, disease management, leadership, quality improvement, care coordination, and involvement in research and policy development (Jameson et al., 2020). As a result of such complexity, efforts to measure school nurse workload remain challenging. However, there exist recent promising efforts to use rigorous methods to measure school nurse workload (Jameson et al., 2020; Jameson et al., 2018) and to incorporate social determinants of health into workload metrics (Daughtry & Engelke, 2017). Measurable aspects of school nurse workload have been identified, including factors related to health conditions and needs of students (e.g., the number of students with chronic disease), social determinants of health (e.g., the number of students who speak English as a second language), and characteristics of the nursing staff (e.g., skill mix) and school community (e.g., absenteeism level; Jameson et al., 2020; Jameson et al., 2018). Better understanding and measurement of school nurse workload is critically needed, given that school nurses have identified excessive workload as the most common barrier to practicing to their fullest scope (Davis et al., 2019).
As a profession, we must consider what can school nurses reasonably impact, given family, school, neighborhood, and sociopolitical contexts that comprise students’ environments. School nursing practice differs from other settings; school nurses care for students over months or years and deliver care within the context of a family, school, and community. Such differences must be considered when interpreting research that assesses school nursing impact. For example, a large body of research demonstrates how hospital nurse staffing can positively impact health outcomes of hospitalized patients (e.g., Aiken et al., 2003; Aiken et al., 2002; Aiken et al., 2014; Rafferty et al., 2007); however, the potential for an intensive care unit nurse to impact infection rates during a brief inpatient stay is very different than the potential for a school nurse to impact a student’s attendance, academic achievement, or adiposity over school years. Hospital infections occur within a short period of time, prescribed environment, and via a direct causal pathway; obesity arises over years; within a home, school, community, and sociopolitical environment; and via a complex multifaceted causal pathway. Thus, research examining school nursing impact on student health outcomes cannot be expected to be as direct and straightforward as hospital nurses’ impact on patient outcomes. It is likely that school nurses can more easily impact proximal clinical indicators (e.g., timeliness of seizure medication administration) than distal indicators (e.g., long-term chronic disease outcomes), given the many factors impacting student health. Such lack of direct impact is inherent to the complexity of addressing complex population health issues in community settings.
Future research is needed to inform understanding of how school nurses impact student outcomes. Based on our results, we suggest three potential avenues for investigation. First, evidence is needed to inform the measurement of school nurse workload. Unlike studies in hospital, simple nurse-to-patient (student) ratios are not appropriate (National Association of School Nurses, 2020); however, measures including only counts of clinical tasks or chronic disease prevalence may be inadequate also. Studies that develop and test workload measures that incorporate the social determinants of health can inform future research demonstrating school nursing impact. To avoid being siloed or duplicative, future work should build off recent efforts to define school nurse workload (Jameson et al., 2020; Jameson et al., 2018), including efforts to consider social determinants of health in staffing decisions (Daughtry & Engelke, 2017). Rigorous methods, entailing collaboration between research teams and frontline school nurses, should be used to test workload measures and explore whether/how such measures need to be tailored across settings or populations. In particular, qualitative work and time-use studies could inform the development of accurate and comprehensive workload measures that reflect school nurses’ practice reality and better capture school nurses’ work related to tasks that are often not accounted for in workload measures, such as communicating with families. Second, future research can use comprehensive workload measures to assess whether and how school nurses impact outcomes including nursesensitive indicators. Studies that explore both proximal (e.g., timely insulin administration) and downstream (e.g., number of diabetes-related emergency room visits) student outcomes can illuminate the whether, when, and how of school nursing impact. Finally, studies with rigorous designs for causal inference would be beneficial. Longitudinal studies that assess how the change in nurse workload impacts change in student outcomes would strengthen the literature. Longitudinal research would be particularly important for assessing the impact on health outcomes that demonstrate gradual change, such as obesity. Randomized controlled trials are less feasible, given the inability to randomize students to school nurses with higher or lower workloads, but rigorous natural experiments may provide a promising approach for future studies. Reliable, validated data collection methods that are designed for and tailored to measure study constructs may be better suited to assessing school nursing impact versus using secondary data not collected for research purposes. Modifications of data collection practices might be needed to capture of the full range of factors, in addition to school nursing, that can impact complex outcomes. Further, expansion of data collection metrics might be needed to capture nursing activities, such as chronic disease management, that often occur informally, frequently, and across diverse methods outside of structured programming.
Our study suggests multiple implications for school nursing. First, it is important to be aware that many of the outcomes school nurses target are complex, distal, and directly impacted by the social determinants of health. Similar to many who work in the field of population health, school nurses’ work is incremental, persistent, and complex. As a result, school nurses may need to consider this nuance when advocating for school nursing’s importance. Their arguments may differ from those of, for example, hospital nurses, who can demonstrate a different type of impact on patient outcomes. This highlights the importance of ensuring school nurses have a role and voice in making decisions about school nurse staffing. Even in settings where stakeholders are not yet fully invested in considering upstream approaches, school nurses can use the results of this study and others (Yoder, 2020) to advocate for the fact that appropriate school nurse workload may improve rates of chronic disease education, reduce absenteeism, and increase time students spend in class. Second, school nurse leaders may benefit from opportunities to consider nurse workload comprehensively when planning staffing for school districts. A comprehensive view may require collaboration with school partners or nurse scientists who have access to school and community-level data, such as the percentage of students who are receiving public assistance or whether the community is considered a medically underserved area. Many leaders consider these factors informally, but opportunities for formal assessment are not available which limits the structure and replicability of workload measures. Third, school nurses and school nurse leaders can continue to prioritize a data-driven approach to measuring their work and impact. Such an approach is critical for recognizing and advancing school nurses’ role as population health leaders within the health and education sectors as well as for advocating for financial and policy support for their role. Finally, nurse scientists and school nurse leaders can seek frontline school nurses’ input during efforts to understand and measure school nurses’ workload and impact. For example, after completion of this study, the study team planned a presentation and dissemination conversations to share results with and gain perspectives of frontline school nurses; such an approach is critical to ensuring that school nursing research is relevant to, informed by, and understood within the context of school nurses’ daily experiences.
Study strengths include the combination of multiple data sources, large and diverse sample, and theory-driven research question. Study limitations include cross-sectional design, use of secondary data not collected for research purposes, and potential lack of generalizability to other settings (e.g., rural or suburban schools).
Our study assessed the association of school nurse workload with student health and academic outcomes among a large and diverse sample of youth using robust multilevel data sources. Findings suggest school nurses may influence proximal outcomes more easily than complex downstream outcomes. As community-based and population health–oriented clinicians, school nurses deliver care that is complex and directly affected by social determinants of health. More research is needed to better understand school nurses’ workload and how school nurses can be supported to best impact the health, well-being, and academic success of children, families, and populations.
The authors would like to acknowledge Erin D. Maughan, PhD, MS, RN, PHNA-BC, FNASN, FAAN at the National Association of School Nurses for her insight into the study results which informed the Discussion section of the article.
All authors contributed to the study conception or design, and acquisition, analysis, or interpretation of the data. KS drafted the manuscript, and all authors critically revised and gave final approval of the manuscript.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The National Association of School Nurses provided funding for this study via an Analysis of Existing School Health Services Data research grant (PI: Krista Schroeder).
Supplemental material for this article is available online.
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Krista Schroeder, PhD, RN, is an Assistant Professor at Department of Nursing, Temple University College of Public Health, Philadelphia, PA, USA.
Ally Young, is a BSN Student Research Assistant at Department of Nursing, Temple University College of Public Health, Philadelphia, PA, USA.
Gail Adman, MPH, BSN, RN, is the Director of Nursing at New York City Department of Health and Mental Hygiene, Office of School Health, Long Island City, NY, USA.
Ann Marie Ashmeade, BSN, RN, is the Deputy Director of Nursing at New York City Department of Health and Mental Hygiene, Office of School Health, Long Island City, NY, USA.
Estherlyn Bonas, BSN, RN, is the Assistant Director of Nursing at New York City Department of Health and Mental Hygiene, Office of School Health, Long Island City, NY, USA.
Sophia E. Day, MA, is a Research Scientist at New York City Department of Health and Mental Hygiene, Office of School Health, Long Island City, NY, USA.
Kevin Konty, PhD, MS, MA, is the Director of Data Science and Research at New York City Department of Health and Mental Hygiene, Office of School Health, Long Island City, NY, USA.
1 Department of Nursing, Temple University College of Public Health, Philadelphia, PA, USA
2 New York City Department of Health and Mental Hygiene, Office of School Health, Long Island City, NY, USA
Corresponding Author:Krista Schroeder, PhD, RN, Department of Nursing, Temple University College of Public Health, 3307 North Broad Street, Philadelphia, PA 19104, USA.Email: krista.schroeder@temple.edu