Trends of Nursing Research on Accidental Falls: A Topic Modeling Analysis

Abstract

This descriptive study analyzed 1849 international and 212 Korean studies to explore the main topics of nursing research on accidental falls. We extracted only nouns from each abstract, and four topics were identified through topic modeling, which were divided into aspects of fall prevention and its consequences. “Fall prevention program and scale” is popular among studies on the validity of fall risk assessment tools and the development of exercise and education programs. “Nursing strategy for fall prevention” is common in studies on nurse education programs and practice guidelines to improve the quality of patient safety care. “Hospitalization by fall injury” is used in studies about delayed discharge, increased costs, and deaths of subjects with fall risk factors hospitalized at medical institutions due to fall-related injuries. “Long-term care facility falls” is popular in studies about interventions to prevent fall injuries that occur in conjunction with dementia in long-term care facilities. It is necessary to establish a system and policy for fall prevention in Korean medical institutions. This study confirms the trends in domestic and international fall-related research, suggesting the need for studies to address insufficient fall-related policies and systems and translational research to be applied in clinical trials.

Trend Research of Maritime Autonomous Surface Ships (MASS) based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

“…In this study, topic cohesion is used to determine the number of topics, and the 'Cv' metric is applied, which is rated as having excellent performance among several similar indices. Topic cohesion is a technique for determining the interpretability of an extracted topic from the words comprising that topic, reflecting the way humans interpret text, and assumes that the higher the average of the pairwise similarity between words in an extracted topic's word set, the higher the topic's cohesion [46]. 'Cv' is a method proposed by Röder et al [47] that depicts a keyword as a context vector expressing its co-occurrence frequency with surrounding terms, and arithmetic averages pairwise similarity between keywords by calculating cosine similarity [47].…”

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

“…In this study, topic cohesion was used to determine the number of topics, and the 'Cv' metric was applied, which is considered to have excellent performance among several similar indices. Topic cohesion is a technique used to determine the interpretability of an extracted topic from the words comprising the topic, reflecting the way in which humans interpret text, and it assumes that the higher the average of the pairwise similarity between words in an extracted topic's word set, the higher the topic's cohesion [45]. 'Cv' is a concept proposed by Röder et al [46] that depicts a keyword as a context vector expressing its co-occurrence frequency with the surrounding terms, and it arithmetically averages the pairwise similarity between keywords by calculating the cosine similarity.…”

Effect of an Educational Program about Fall Prevention and Pain Assessment on Nurses' Performance and Critically Ill Patients' Outcome

“…Thus, many studies could not be included, which limited the derivation of integrated results. Further, since falls are caused by a combination of intrinsic and extrinsic factors, an integrated body of knowledge such as interrelationships of issues and changes and trends over time needs to be utilized in clinical practice (Seo et al, 2021).…”