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Abstract
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Proceedings of the 14th World Conference on Injury Prevention and Safety Promotion (Safety 2022)
Objective The development of countermeasures for preventing childhood injuries requires prioritizing injury situations to be prevented based on situation data. Conventionally, a R-Map is well known as a method for prioritizing injuries from the viewpoint of both frequency and severity. However, it is difficult to apply the method to a large amount of text data describing injury situations. This study proposes a situational R-Map analysis, which is a new method for prioritizing injury situations by integrating a R-Map analysis method and a text mining method.
Methods In this study, a situational R-Map analysis was applied to the data on the ten types (e.g., bars, slides, sandboxes, and jungle gyms) of playground-equipment-related injuries that occurred in elementary schools. The data were collected by the Japan Sport Council (JSC) from schools across Japan in 2018. The authors selected playground-related cases (about 25,000 cases) from the 1 million cases.
Results As a result of the analysis, injury situations with high priority were found in ten types of equipment. For example, 'failing to land and sticking his/her hand to the ground during the long jump in physical exercise classes' was an example of sandbox-related situations with a high priority for preventing bone fractures. This result shows that the situational R-Map analysis can be used to automatically extract high-priority injury situations from big data, which is conventionally difficult to analyze manually. JSC created a brochure on injury situations and preventive measures clarified by the proposed method and disseminated it to more than 2,000 municipalities throughout Japan.
Language: en |