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Abstract Background Child safety in schools is paramount for decision-makers globally, with a focus on ensuring children return home safely. However, the prevalent issue of injuries across educational systems demands a comprehensive investigation into their causes, incorporating interdisciplinary perspectives and social dynamics, to develop effective prevention strategies. The objective of this study is to comprehensively analyze the factors contributing to school-related injuries and examine the impact of school climate on student safety. By employing advanced data analysis techniques, the research aims to develop targeted, effective strategies to enhance child safety in educational settings. This research aims to develop a multidimensional taxonomy to understand child injuries in elementary schools better, enhancing precision in decision-making. Methods Data from 363 Israeli primary schools and 10,855 school injuries attended to by MDA, the principal EMS provider, were analyzed. The study utilized a two-level taxonomy, employing clustering methodology to classify schools into distinct climate "patterns," with each pattern further delineating school injury characteristics into sub-patterns. The chosen method proved effective in revealing nuanced relationships between school injuries and climate characteristics. Results Analysis revealed five school climate clusters, ranging from "good" to "bad," each exhibiting two homogeneous sub-clusters of school injuries. Schools with a "positive" climate witnessed boys predominantly experiencing head injuries during breaks, while girls often sustained limb injuries from playing in corridors. Conversely, within the "negative" climate cluster, subgroups emerged based on injury nature, whether linked to playing or falling from a height. Conclusion The research delineates a nuanced association between school climate and injury rates, emphasizing the necessity for sophisticated analytical techniques beyond conventional methodologies. Utilizing a diverse dataset from various disciplines, the study highlights the multidimensional aspects of school health. The developed taxonomy reveals the complex dynamics within school environments, advocating for customized health policies to mitigate injuries. Critical findings prompt a reevaluation of established assumptions about the school climate-injury relationship, informing strategic policymaking. For example, it suggests collaboration to enhance school safety through targeted, gender-sensitive interventions and improvements. Integrating different data sources offers a holistic understanding crucial for effective health policy formulation in educational contexts.

BackgroundSmartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers’ availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched. ObjectiveThis study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert. MethodsWe developed and compared 4 analytical models to predict VFRs’ response behaviors based on emergency event characteristics, volunteers’ demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder’s previous behavior. ResultsThe highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs’ demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs’ previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy. ConclusionsVFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.