Abstract
Introduction:
Social vulnerability is a systemic barrier to student success. School nurses are critical members of the school workforce, yet variation in staffing and their relationship to student outcomes is not well studied.
The goal of this project was to examine the number of school nursing staff in the state of Georgia for relationships across social vulnerability and school quality.
Methods:
A secondary data analysis was conducted using linear regression followed by moderation and mediation analyses to examine the relationships among the study variables.
Findings:
Linear regression revealed that school nurse staff headcounts and social vulnerability were statistically significant factors in the outcome of school quality in Georgia. Moderation and Mediation analysis revealed that school nurses are key actors in interrupting the negative consequences of social vulnerability but are hindered due to understaffing.
Discussion/Recommendations/Conclusions:
School nurses play a key role in student success, and it is essential that all children have access to this precious resource.
Keywords: school nursing, school health, adolescent health, child health, public health
INTRODUCTION
Professional school nurses represent an often-overlooked health resource within public schools across the United States. These nurses care for the entire school community by coordinating care, leading quality improvement initiatives, collaborating with community and public health partners, and assuming a leadership role in health promotion (NASN, 2022). Contemporary healthcare is inherently interdisciplinary and the school health setting encompasses a broad, multi-specialty scope of practice. As student care needs become more complex, oversight and skilled management from trained nursing professionals is essential (Denke & Winkleblack, 2020). The recently updated School Nursing Practice Framework (NASN, 2024) serves as a tool for understanding this; delineating how nurses in this setting meet the complex and diverse needs of their populations to achieve health, safety and academic readiness. Still, despite this value-add and broad appeal, many students do not have access to a professional school nurse. Nationally there are more than 78,000 school nurses, yet many serve in part-time roles and many students lack access to a school nurse entirely (Willgerodt et al., 2024). The most common reasons provided were insufficient school size or budgetary restrictions (Willgerodt et al., 2024). As such, nursing care and associated tasks may be inappropriately delegated to other members of the school community, such as teachers, athletic trainers, administrators, or volunteers who lack nursing training (Willgerodt et al., 2024). Parents of students who have complex care needs are intimately aware of how important school nurses are to the health and safety of their children (Morales Ruíz et al., 2025). For children living in communities with high levels of social vulnerability with no school nurse available, the risk of poor academic outcomes due to mismanagement of acute illness and future negative impacts on overall long-term health, is further compounded (Schroeder et al., 2018).
In the realm of child health, social vulnerability has been identified as an important concept for improving the future of the community (Longhi et al., 2021). Social vulnerability can be understood as a systematic way to understand how the different social determinants of health interplay to compound or reduce risk in the context of an adverse event (Mah et al., 2023). This aligns with the perspective of syndemics which views adverse health not solely as the result of individual decision-making, but as the result of the interplay of social determinants of health, social, and political adversity (Willen et al., 2017). When considering the schools and access to pivotal health resources, such as the school nurse, this perspective is particularly relevant. Children represent the future of the community and if their health can be improved, the overall population health can be improved (Longhi et al., 2021). This is why the work to improve overall community health must begin where the children spend the majority of their time, the schools, and why a close examination of the health resources they have available is merited.
In recent years, the concept of social vulnerability has been gaining traction as a new framework through which to analyze disparities (Mah et al., 2023). Social vulnerability has been used in disaster preparedness efforts to understand how a community would react in a crisis by analyzing access to resources, socio-economic status, social capital, and social engagement (Mah et al., 2023). However, viewing communities in terms of social vulnerability has applicability beyond disaster and crises. COVID-19 placed a spotlight on school environments and demonstrated the importance of considering school environments to understand social vulnerability (Larsen et al., 2022). Social vulnerability is an important concept in understanding the ability of an individual or community to be empowered in regard to their health and the ability of an individual or community to navigate challenging circumstances (Suleimany et al., 2022).
An important factor to consider when analyzing social vulnerability across neighborhoods is the effect of the school and school nursing as a key resource within the school. Academic achievement is known to have a relationship with neighborhood factors such as poverty and demographics (Davis & Welcher, 2013; Wodtke et al., 2023). School quality has long been posed as an important measure of how well schools are meeting the needs of the community. So much so, that states are mandated by federal law (Every Student Succeeds Act / ESSA) to report a measure so that the community can have a role in school policy development and advocate for community needs. Wodtke and colleagues (2023) posited that theoretically, broader school factors (i.e. school quality and number of teachers) would play a significant role in mediating negative neighborhood effects, but when modeled, found that something was missing. Wodtke and colleagues (2023) posited that this missing resource might be located within the elementary schools, a location where incidentally, school nurses are particularly active, as elementary school children are often not able to manage chronic conditions independently and rely on the school nurse for care (Lineberry & Ickes, 2015). Therefore it is possible that the missing variable proposed by Wodtke and colleagues (2023) is the often-overlooked resource, the school nurse, as they may be an important element in reducing the negative effects of social vulnerability by having a positive effect on school quality.
This project was part of a larger investigation into the role of school nurses in the state of Georgia (Hamilton et al., 2025) to understand their impact on student academic outcomes and health prior to the pandemic. Prior findings discovered a statistically significant relationship between the number of school nurses available within school districts and school quality measures (Hamilton et al., 2025). The noteworthy finding indicated that the school nurse may have a larger impact on students than previously anticipated, especially in communities with high social vulnerability. The purpose of this study is to examine the impact of social vulnerability in combination with school nursing on student outcomes to understand if school nursing poses as a meaningful intervention for improving school quality. This was answered through two primary research questions:
Does school nursing have a direct and indirect relationship with school quality?
Do school nurses play a role in mediating and moderating the effects of social vulnerability on school quality?
METHODS
Theoretical Framework – Transcultural Nursing Theory
Transcultural Nursing Theory (TCN), also known as the Sunrise Model, was used as the guiding theoretical framework for selection of variables and interpretation of findings (Leininger, 1988). TCN depicts outcomes as part of a nexus of cultural, social, and structural factors, specifically identifying education as a key factor in attaining optimal health. In addition to this, the role of the nurse is highlighted as a key actor in outcomes, specifically if the nurse is able to foster trust and communication through the lens of understanding. It is this relationship between nurses, educational variables, and outcomes in the context of broader cultural, social, and structural factors that this analysis is explicitly testing.
Measures
School Health Staff Headcounts:
All data used within this study included the most recent data available pre-COVID-19 pandemic. The COVID-19 pandemic radically changed the school environment (Morse et al., 2025), so the data in this study were unaffected by the changes that occurred both within school nursing and the school environment during the pandemic. Secondary data was collected from the 2019–2020 Georgia Department of Education (GADOE) for district-level headcounts of school nurses (SNs), nursing assistants (NAs), and special education nurses (SENs) at the school district level. These headcounts were based on headcounts with associated job codes 409 for SNs, 444 for NAs, and 486 for SEN. Licensure requirements for the different job codes vary district to district so these job codes represented the most accurate count of licensed personnel, non-licensed, and specialized school health workers that is accessible at the state level. The SNs and SENs represent licensed RNs or LPNs while the NAs could include paramedics, medical technicians, certified nursing assistants, or unlicensed personnel. The state of Georgia does not track placement of school health workers at individual schools. Currently no law exists that requires school health workers within schools, so the best data was available at the district level.
College and Career Readiness Performance Index:
Secondary data was additionally collected from GADOE for the 2019 College and Career Readiness Performance Index (GADOE, 2019). (CCRPI) which is how schools and districts across the state of Georgia measure quality in compliance with the ESSA. The CCRPI is a holistic measure that captures five main components: Content Mastery, Progress, Closing Gaps, Readiness, and Graduation Rate through the synthesis of different measures including school climate and financial efficiency in addition to test scores and college placements. Schools and districts are given quality ratings from 0–100. In this study, district-level data was analyzed. There is slight temporal misalignment with GADOE staff headcounts due to administrative shutdowns related to the COVID-19 pandemic. However, staffing structures, particularly among school health staff, are historically stable in Georgia so these headcounts serve as a reliable proxy for the school environment surrounding the assessment window.
Social Vulnerability Index:
The Centers for Disease Control (CDC) Social Vulnerability Index (SVI)(CDC, 2020) was collected at the county level for the year 2020. This measure was collected from the CDC website in 2022 prior to changes being implemented to the measure in early 2025. The SVI uses 16 United States (US) Census variables from the American Community Survey (ACS) to estimate communities that may need additional assistance prior to, during, and following a disaster. It is a composite measure that captures elements of socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation.
Rural-Urban Continuum Codes:
In addition to this, rurality was captured using data from the 2020 Rural-Urban Continuum Codes(USDA, 2020) (RUCC) from the US Department of Agriculture. RUCC codes identify urban versus non-urban areas in terms of proximity to metropolitan areas and level of urbanity using primarily population density estimates. This study was deemed exempt by University of South Carolina Institutional Review Board.
Inferential Analyses
SVI and RUCC were used at the county level, and to optimize their use, school districts were sorted by county. School district CCRPI score and staff headcounts were averaged in counties where there was more than one school district present to properly fit SVI and RUCC scores and not duplicate geographical areas.
Descriptive statistics were then calculated. After checking assumptions, linear regression was conducted to analyze the relationship between variables and the outcome of CCRPI. To further investigate the relationship between SVI and SN headcounts, SVI and SN were centered, and the interaction term was calculated. Following this, an analysis of moderation was conducted using the PROCESSv.5 model 1 (simple moderation) with the Johnson Neyman test and a mediation analysis was conducted using the PROCESSv.5 model 4 (simple mediation) (Hayes, 2022). PROCESSv.5 model 1 was used to answer the question, when and for whom does school nurse headcounts relate to College and Career Readiness Performance Index? PROCESSv.5 model 4 was used to answer the question, how and why does school nurse headcounts impact College and Career Readiness Performance Index? All analyses were conducted in SPSS 31.
FINDINGS
Descriptive Statistics
School districts were sorted by county. Headcounts and CCRPI scores were averaged to best fit RUCC and SVI measures and prevent over representation in the data. Across the state of Georgia there were 20 counties that have more than one school district. Of these, only 1 county (Jackson County) has three districts. The remaining 19 had two. Descriptives from this subsample are shown in Table 1.
Table 1.
Descriptives; Georgia Counties with 2 Districts (2019–2020) Georgia Department of Education
| Variable | Range | Min-Max | Mean | Standard Deviation | Skewness | Kurtosis |
|---|---|---|---|---|---|---|
|
| ||||||
| College and Career Readiness Performance Index | 24.4 | 0–24.4 | 9.85 | 6.30 | 0.46 | 0.22 |
| School Nurse Headcount | 116 | 0–116 | 21.56 | 32.96 | 2.11 | 3.82 |
| Nursing Assistant Headcount | 101 | 0–101 | 6.26 | 23.16 | 4.2 | 18.15 |
| Special Education Nurse Headcount | 26 | 0–26 | 2.58 | 7.43 | 2.85 | 7.02 |
After sorting school districts by county and averaging headcounts and CCRPI, there were a total of 159 school districts with a total number of 1,261 SNs, 250.5 NAs, and 39.5 SEN. The lowest CCRPI score was 56% and the highest was 86.4%. Urban school districts had more school health staff on average when compared to rural school districts. In addition, urban school districts scored higher on CCRPI when compared to rural school districts. Across the state, the average score on CCRPI was 72.33%. More details are provided in Table 2.
Table 2.
Sample Descriptive Statistics
| Variable | N | M | Mdn | SD | Min/Max | IQR | Shapiro-Wilk |
|---|---|---|---|---|---|---|---|
|
| |||||||
| CCRPI Single Score | 159 | 72.90 | 72.50 | 7.47 | 56–93 | 10 | 0.99 |
| RUCC | 159 | 4.45 | 4 | 2.56 | 0–9 | 4 | 0.91 |
| SVI | 159 | 0.50 | 0.50 | 0.29 | 0–1 | 0.50 | 0.96 |
| SN Headcount | 159 | 7.93 | 4 | 10.93 | 0–58 | 6 | 0.64 |
| NA Headcount | 159 | 1.58 | 0 | 8.30 | 0–62 | 0 | 0.19 |
| SEN Headcount | 159 | 0.25 | 0 | 1.40 | 0–13 | 0 | 0.17 |
College and Career Readiness Performance Index = CCRPI
Rural-Urban Continuum Codes = RUCC
Social Vulnerability Index = SVI
School Nurse = SN
Nursing Assistant = NA
SEN = Special Education Nurse
Linear Regression
CCRPI, SN headcounts, NA headcounts, SEN headcounts, SVI, and RUCC were linearly regressed using SPSS 31. The model is shown in Table 3. CCRPI was shown to be normally distributed (Shapiro-Wilk=0.99; skew=0.73; kurtosis=−0.26–0.38). VIF and Collinearity Tolerance indicated that there was no problematic multicollinearity present. 39.5% of the variance was explained by the model (R2). The model was statistically significant (p=<.001). The model studentized residuals indicated normal distribution via Shapiro-Wilk (0.99), skewedness, (−0.13-0.19) and kurtosis (−0.19–0.38). Two variables in model displayed statistical significance (p<0.05), SN headcounts (p=0.02) and SVI (p=<0.001).
Table 3.
Regression Model School Quality and School Health Headcounts
| Model Summary | ||||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| R= 0.628 | R2=0.395 | Adjusted R2=0.38 | Std.Error of Estimate=5.90 | Durbin-Watson=2.04 | ||||
|
| ||||||||
| ANOVA Sum of Squares | df | Mean Square | F | Sig. | ||||
| Regression | 3477.46 | 5 | 695.49 | 19.95 | <0.001 | |||
| Residual | 5333.12 | 153 | 34.86 | |||||
| Total | 8810.58 | |||||||
| Coefficients Unstd. B | Coefficent Std. Error | Std. Coefficients Beta | t | sig | Confidence Interval (CI) | VIF | Collinearity Tolerance | |
| Constant | 78.24 | 1.39 | 56.29 | <0.001 | 75.49–80.98 | |||
| SN Headcount | 0.13 | 0.57 | 0.19 | 2.35 | 0.02 | 0.02–0.24 | 2.37 | 0.42 |
| NA Headcount | 0.02 | 0.06 | 0.02 | 0.33 | 0.74 | −0.01–0.14 | 1.05 | 0.95 |
| SEN Headcount | 0.22 | 0.39 | 0.04 | 0.57 | 0.57 | −0.55–0.99 | 2.1 | 0.48 |
| SVI | −14.64 | 1.85 | −0.57 | −7.91 | <0.001 | −18.29–10.99 | 1.12 | 0.89 |
| RUCC | 0.18 | 0.23 | 0.06 | 0.81 | 0.42 | −0.26–0.63 | 1.34 | 0.75 |
| Studentized Residuals | ||||||||
| Skewedness | −0.28–0.19 | |||||||
| Kurtosis | −0.19–0.38 | |||||||
| Shapiro-Wilk | 0.99 | |||||||
College and Career Readiness Performance Index = CCRPI
Rural-Urban Continuum Codes = RUCC
Social Vulnerability Index = SVI
School Nurse = SN
Nursing Assistant = NA
SEN = Special Education Nurse
Data was collected from the Georgia Department of Education, the Centers for Disease Control, and United States Department of Agriculture
Simple Moderation: School Nurse Effect on College and Career Readiness Performance Index
Moderation was chosen to analyze the effect of SNs on CCRPI depending on the amount of social vulnerability (SVI) in the community. The full model is shown in Table 3. The R2 for the model indicated a significant amount of variance explained by the model (R2=40.8%). The model showed statistical significance (p=0.00). The interaction term was significant and showed a negative relationship (p=0.04, b=−0.34). The simple slopes (conditional effects) indicated that as SVI increases, the effect of SNs disappears (low = 0.2, p=0.00; medium = 0.09, p= 0.06; high = −0.02, p=0.79). The Johnson-Neyman boundary (0.49) indicated that the effect of SNs was significant for about half of the sample, the lower SVI schools, or the schools that have less social vulnerability.
Simple Mediation: School Nurse Effect between Social Vulnerability Index and College and Career Readiness Performance Index
Mediation was chosen to further investigate the relationship between variables and is shown in Table 4. This analysis sought to investigate the effect of SNs between SVI and CCRPI. Path A was statistically significant (p=0.00) and explained about 8% of the variance (R2=0.08). The effect of SNs demonstrated statistical significance (p=0.00; b=−10.48). This indicated that higher social vulnerability was correlated with significantly reduced SN headcounts. Path B was statistically significant (p=0.00) and explained about 39% (R2=0.39) of the variance. Similar to Path A, the effect of SNs was also statistically significant (p=0.00) but displayed a positive effect (0.14). This indicated that more nurses was correlated with improved CCRPI, regardless of SVI. The direct effect (c’) was statistically significant (p=0.00) and demonstrated a negative relationship (−13.94) showing that SVI still impacts CCRPI beyond staffing. The number of bootstrap samples for percentile bootstrap confidence intervals was 5,000. The indirect effect was −1.43 and the confidence interval (−2.92- −0.37) indicated significant mediation, meaning that part of SVI’s negative effect operates through reduced SN headcounts.
Table 4.
Moderation Analysis (PROCESS Procedure Version 5.0)
| Model: 1 Y: CCRPI X: SNHC W: SVI N=159 |
||||||
|---|---|---|---|---|---|---|
|
| ||||||
| Model Summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.64 | 0.41 | 33.68 | 35.53 | 3 | 155 | 0.00 |
| Model | ||||||
| coeff | se | t | p | LLCI | ULCI | |
| Constant | 77.56 | 1.23 | 63.18 | 0.00 | 75.13 | 79.98 |
| SNHC | 0.26 | 0.07 | 3.51 | 0.00 | 0.12 | 0.41 |
| SVI | −11.44 | 2.05 | −5.59 | 0.00 | −15.48 | −7.39 |
| Interaction | −0.34 | 0.16 | −2.09 | 0.04 | −0.66 | −0.02 |
| Tests of highest order unconditional interactions | ||||||
| R2-chng | F | df1 | df2 | p | ||
| X*W | 0.02 | 4.39 | 1 | 155 | 0.04 | |
| Conditional effects of the focal predictor at values of the moderator | ||||||
| SVI | Effect | se | t | p | LLCI | ULCI |
| 0.16 | 0.21 | 0.06 | 3.73 | 0.00 | 0.10 | 0.32 |
| 0.50 | 0.09 | 0.05 | 1.90 | 0.06 | −0.00 | 0.19 |
| 0.84 | −0.02 | 0.09 | −0.26 | 0.80 | −0.20 | 0.15 |
| Moderator values defining Johnson-Neyman significance regions | ||||||
| Value | % below | % above | ||||
| 0.49 | 49.06 | 50.94 | ||||
College and Career Readiness Performance Index = CCRPI
Rural-Urban Continuum Codes = RUCC
Social Vulnerability Index = SVI
School Nurse = SN
Nursing Assistant = NA
SEN = Special Education Nurse
Data was collected from the Georgia Department of Education, the Centers for Disease Control, and United States Department of Agriculture
DISCUSSION AND RECOMMENDATIONS
This analysis revealed that school nurses play a pivotal role in school quality acting both as a moderator and mediator for social vulnerability. Study findings are summarized in Figure 1. This analysis provides compelling evidence that school nurses have the potential to significantly reduce the negative consequences of high social vulnerability but understaffing limits this impact. To our knowledge, this paper is the first to explicate the relationship between SNs and school outcomes within the state of Georgia and to demonstrate that SN headcounts meaningfully contribute to improvements in school quality. Even more compelling, the data in this analysis pre-dates the COVID-19 pandemic, which was detrimental to school environments (Houghton et al., 2022). It is entirely possible that the positive effect of SNs has been magnified, post pandemic, as socially vulnerable communities have experienced amplification of disenfranchisement(Rahma, 2025).
Figure 1.
School Nurses as a Moderator/Mediator of Social Vulnerability
The moderation analysis found that having more SNs was beneficial for increasing levels of CCRPI, but the benefit that SNs played largely disappeared in communities that have high levels of SVI (i.e. lower income, less access to resources). Currently, the strong positive effect of SNs is only benefiting communities with low SVI (i.e. higher income, more access to resources), underscoring a possible disparity in resource allocation. Further, the mediation analysis found that higher SVI does harm school performance and that SVI level could explain why there are fewer SNs in schools. It is possible that this is driven by understaffing of this resource.
Our findings purport that SNs can improve student outcomes and foster optimal academic performance, yet their impact is limited by structural and contextual barriers. These findings could be driven by numerous factors within the socio-political environment of Georgia schools. Poverty and lack of access to community resources is a known risk factor for student performance and achievement (Grau & García-Raga, 2017). This is why interventions to mitigate this impact often center around capacity building (Grau & García-Raga, 2017) and could explain the relationship between SVI and CCRPI. SNs function as vital agents to foster educational equity, especially among communities with fewer resources. This analysis indicates that capacity building efforts that integrate the school nurse could have an impact on overall school performance.
Although Every Student Succeeds Act (ESSA) allocates funding to support student health, schools are not required to use these funds for nursing staff (NASN, 2025c). This may represent a missed opportunity to invest in nursing in districts where funding is allocated towards other priorities (Ohneck et al., 2024). For example, in a recent study in an urban school district, authors found that investment in school nursing resulted in a return on investment that was conceptually driven by the interdisciplinary work that SNs perform (Ohneck et al., 2024).
Our findings add to the growing evidence that investing in school nursing is a critical opportunity to improve public health. SNs play a central role in managing acute and chronic conditions, addressing student mental health and well-being, and supporting safety planning. They also serve as a key link between the education and healthcare systems, helping to coordinate care, facilitate access to services, and address social determinants of health that affect children and families (NASN, 2024). SNs perform many functions within the school environment that may be driving the findings of this analysis, specifically the positive relationship between CCRPI and school nurse headcounts. SNs partner with educators to facilitate student attendance and engagement (Yoder, 2020)and serve as the primary health experts in the school setting (Willgerodt et al., 2024). With up to 40% of students living with chronic health needs such as asthma, diabetes, epilepsy, and behavioral health challenges, the absence of adequate nursing support jeopardizes both health and academic achievement (CDC, 2024).
Despite these benefits, workforce challenges persist in school nursing, including shortages, high student-to-nurse ratios, and variability in staffing models across districts(Kurtzman et al., 2022). Solutions to these challenges remain complex. In our model, RUCC was a non-significant variable. As RUCC is a measure of population density and pupil headcounts are often a factor population density, this furthers the arguments that NASN has made (NASN, 2025b) to move away from nurse-student ratios based solely on pupil headcounts and move towards an acuity model for school nurse staffing. Our model indicates that this acuity could be measured using SVI, as it has shown promise as a measure of health disparities (Higginbotham et al., 2025). However, more work replicating this in other states is warranted to clarify the suitability of SVI in school nurse staffing calculations.
Additionally, this analysis provides evidence that the activities of school nurses cannot be substituted by certificate-level certified medical professionals as NAs were not statistically significant in the model. SENs, who are registered nurses, were not statistically significant either, but this could be due to the fact that this analysis was conducted at the district level. SEN impact is more likely to be captured in an analysis that looks at individual schools due to the size of student population that SENs are tasked with serving.
Future research should focus on rigorous longitudinal and quasi-experimental studies to establish causal links between the activities of school nurses, staffing and student outcomes to further provide insights. Additionally, standardized national data on SN workforce supply, staffing ratios, and scope of practice are needed to inform policy decisions and build the case for investing in school nursing on a national level (NASN, 2025a). These findings are critically important for advancing school nursing in communities that have high social vulnerability and building the rationale for enhanced school nurse staffing ratios.
LIMITATIONS
This study has some notable limitations. The CCRPI measures school quality specifically in the state of Georgia and is not comparable to other states. Additionally, the CCRPI has experienced changes to the measurement instrument and is not comparable to other years. Therefore, this cross-sectional study is non-generalizable outside of the study timeframe. Additionally, data collected for CCRPI, staffing headcounts, and SVI is not collected simultaneously, so it is possible that data collection timeframes do not match perfectly. This analysis does not include data that includes licensure data and the job code associated with the SN position could refer to licensed practical nurses (LPNs) or registered nurses (RNs). This limits understanding of specific licensure and educational attainment related factors that could be driving findings. In terms of workforce, this analysis was limited to headcounts so there are limited insights on the exact staffing ratios that would be beneficial for optimizing CCRPI. Headcounts also fail to show which employees are full versus part time, so this analysis offers limited insights into the work capacity of school nurses staffed in Georgia. Additionally, this data lacks information regarding educational preparation. Therefore, insights and variation related to bachelors preparation versus other forms of preparation are limited. This data is limited to the school district level and that counties who had numerous school districts were averaged. Individual school staffing patterns were not able to be captured by this analysis.
CONCLUSIONS
This analysis underscores the importance of SNs as important public health actors that significantly impact school quality. SNs were identified as a key variable in disrupting the negative consequences of social vulnerability, but their impact is being hindered by understaffing. This is in line with the mounting evidence that proper nurse staffing is an important variable in the delivery and quality of healthcare regardless of setting.
Table 5.
Mediation Analysis (PROCESS Procedure 5.0)
| Model: 4 Y: CCRPI X: SVI M: SNHC N=159 |
||||||
|---|---|---|---|---|---|---|
|
| ||||||
| Outcome Variable: SNHC | ||||||
| Model Summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.28 | 0.08 | 110.91 | 13.07 | 1 | 157 | 0.00 |
| Model | ||||||
| Coeff | se | t | p | LLCI | ULCI | |
| Constant | 13.14 | 1.67 | 7.89 | 0.00 | 9.85 | 16.43 |
| SVI | −10.48 | 2.90 | −3.62 | 0.00 | −16.20 | −4.75 |
| Outcome Variable: CCRPI | ||||||
| Model Summary | ||||||
| R | R2 | MSE | F | df1 | df2 | p |
| 0.63 | 0.39 | 34.41 | 50.02 | 2 | 156 | 0.00 |
| Model | ||||||
| coeff | se | t | p | LLCI | ULCI | |
| constant | 78.76 | 1.10 | 71.82 | 0.00 | 76.59 | 80.93 |
| SVI | −13.94 | 1.68 | −8.30 | 0.00 | −17.26 | −10.62 |
| SNHC | 0.14 | 0.04 | 3.06 | 0.00 | 0.05 | 0.22 |
| Direct & Indirect Effects of X on Y | ||||||
| Direct Effect of X on Y | ||||||
| Effect | se | t | p | LLCI | ULCI | |
| −13.94 | 1.68 | −8.30 | 0.00 | −17.26 | −10.62 | |
| Indirect Effect of X on Y | ||||||
| Effect | BootSE | BootLLCI | BootULCI | |||
| SNHC | −1.43 | 0.65 | −2.92 | −0.37 | ||
Level of Confidence for all confidence intervals: 95
Number of bootstrap samples for percentile bootstrap CI: 5,000
College and Career Readiness Performance Index = CCRPI
Rural-Urban Continuum Codes = RUCC
Social Vulnerability Index = SVI
School Nurse = SN
Nursing Assistant = NA
SEN = Special Education Nurse
Data was collected from the Georgia Department of Education, the Centers for Disease Control, and United States Department of Agriculture
Highlights.
School nurses are important actors in facilitating school health programming
This analysis indicates that school nurses interrupt the negative consequences of social vulnerability
School nurses are underutilized, often due to budgetary constraints
This analysis demonstrates that school nursing in an important investment for the health of communities.
Funding
Partially funded through the National Institutes of Health National Institute of Nursing Research grant number T32NR020778 (PIs: D. Bruner and V. Hertzberg) for trainee Bridget Hamilton. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Declaration of Competing Interest
The authors have no conflicts of interest to disclose
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
There are no conflicts of interest
This research was deemed exempt/ non-human subjects research by University of South Carolina IRB
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