School Nurses and Chronic Absenteeism in Schools: A Qualitative Study on Experiences, Perspectives, and Roles.

Regular attendance is integral for students' academic success; it also affects adolescents' physical and mental health. Very few studies consider (a) differences between partial- and full-day absences regarding chronic absenteeism (CA; missing school ≥15 days in an academic year); or (b) roles of school nurses in addressing student absenteeism. This study describes school nurses' perceptions of factors related to CA and differences between partial- and full-day absences by analyzing qualitative data from six focus groups with 21 Midwestern school nurses. Four themes emerged: Absenteeism at the intersection of family and health; Absenteeism at the intersection of family and school; Absenteeism at the intersection of family and ecological systems; and School nurse roles in supporting chronically absent students. Findings highlight the importance of school nurses by describing their role in identifying students who are at risk for CA, allowing for effective assessment and intervention with students before patterns of CA become habitual.

Data-Driven Analytics to Discover APRN's Impact on Nursing Home Hospitalization: Causal Discovery Analysis.

OBJECTIVES: Research shows advanced practice registered nurses (APRNs) embedded in nursing homes (NHs) reduce resident hospitalizations. However, the specific APRN activities that reduce hospitalizations have not been adequately investigated. This study aims to identify the causal links between APRN activities and NHs resident hospitalization. The study also examined relationships among other variables, including advanced directives, clinical diagnosis, and length of hospitalization. DESIGN: Secondary data analysis. SETTING AND PARTICIPANTS: Residents of NHs participating in the Missouri Quality Initiative for Nursing Homes, 2016-2019. METHODS: We performed a secondary analysis of data from the Missouri Quality Initiative for Nursing Homes Intervention using causal discovery analysis, a machine learning, data-driven technique to determine causal relationships across data. The resident roster and INTERACT resident hospitalization datasets were combined to create the final dataset. Variables in the analysis model were divided into before and after hospitalization. Expert consensus was used to validate and interpret the outcomes. RESULTS: The research team analyzed 1161 hospitalization events and their associated NH activities. APRNs evaluated NH residents before a transfer, expedited follow-up nursing assessments, and authorized hospitalization when necessary. No significant causal relationships were found between APRN activities and the clinical diagnosis of a resident. The analysis also showed multifaceted relationships related to having advanced directives and duration of hospitalization. CONCLUSIONS AND IMPLICATIONS: This study demonstrated the importance of APRNs embedded in NHs to improve resident outcomes. APRNs in NHs can facilitate communication and collaboration among the nursing team, leading to early identification and treatment for resident status changes. APRNs can also initiate more timely transfers by reducing the need for physician authorization. These findings emphasize the crucial role of APRNs in NHs and suggest that budgeting for APRN services may be an effective strategy to reduce hospitalizations. Additional findings regarding advance directives are discussed.

Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students.

Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in "The Kids and Teens at School" (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences. A total of 18 risk and protective variables were identified; all 18 variables were characteristics of the microsystem or mesosystem, emphasizing school absences' proximity to variables within inner ecological systems rather than the exosystem or macrosystem. Implications for future studies and health infrastructure are discussed.

The efficacy of mobile health interventions used to manage acute or chronic pain: A systematic review.

Chronic pain is a significant health issue that affects approximately 50 million adults in the United States. Traditional interventions are not always an effective treatment strategy for pain control. However, the wide adoption of smartphones and the rapid growth of health information technologies over the past decade have created opportunities to use mobile health (mHealth) applications (apps) for pain tracking and self-management. In this PRISMA-compliant systematic review, we assessed the current U.S.-based research on pain-related mHealth apps to describe the app components and determine the efficacy of these interventions for persons with acute or chronic pain. We conducted a comprehensive search of five databases based on methodological guidelines from the Joanna Briggs Institute. We included articles reporting original data on mHealth interventions with pain intensity as a primary or secondary outcome and excluded articles that utilized multimodal interventions. Of the original 4959 articles, only five studies met the eligibility criteria. Most of the interventions included feasibility or pilot studies, and all studies were published between 2015 and 2018. Two of the five studies used visual analog scales. Only two of the studies reported statistically significant pain intensity outcomes, and considerable heterogeneity between the studies limited our ability to generalize findings or conduct a meta-analysis. Research investigating the components and efficacy of pain-related mHealth apps as interventions is an emerging field. To better understand the potential clinical benefits of mHealth apps designed to manage pain, further research is needed.