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역할극을 활용한 간호실습교육의 효과: 체계적 문헌고찰과 메타분석
서윤암,윤상후,김영아 한국데이터정보과학회 2019 한국데이터정보과학회지 Vol.30 No.2
This study is to identify the current status of nursing practice education using role-plays performed in Korea and to identify the effectiveness of education. Thirteen experimental studies were finally analyzed out of the reported journal articles and unpublished thesis by April 2018. The design of all studies was non-equivalent control group pre-post test design. The study participants studied only nurses, recruited nurses and doctors together, studied nursing and social welfare for college students, and conducted 10 for nursing college students. The sample size was 20~136 people (mean: 46.1) and 20~136 controls (mean: 45.2), and the number of role-play scenarios was 1~8 (mean: 3.6). The six most frequently measured dependent variables were interpersonal ability, communication skill, problem-solving ability, self-directed learning ability, self-efficacy, and job satisfaction. The meta-analysis showed statistically significant high-level effect sizes on the four dependent variables excluding self-directed learning ability and job satisfaction. This study identified the effects of nursing practice education using role-play as one of the more popular methods of simulated practical education. This could be used as a basis for the evidence necessary to optimize the effectiveness of nursing practice education. 본 연구의 목적은 국내에서 수행된 역할극을 활용한 간호실습교육의 현황을 파악하고 교육의 효과를 알아보기 위함이다. 2018년 4월까지 보고된 국내 학위 및 학술지 논문 중, 13편의 실험연구를 선별하여 최종 분석하였다. 모든 연구의 설계는 비동등성 대조군전후실험설계였다. 연구대상자는 간호사 단독대상연구가 1편, 간호사와 의사를 같이 모집한 연구가 1편, 간호학과 및 사회복지학과 대학생을 대상으로 1편, 간호학과 대학생만을 대상으로 10편이 수행되었다. 표본크기는 실험군 20~136명 (평균 46.1), 대조군 20~136명 (평균 45.2)이었고, 역할극 시나리오의 수는 1~8개 (평균 3.6) 였다. 가장 많이 측정된 종속변수 6가지는 대인관계능력, 의사소통기술, 문제해결능력, 자기주도적 학습능력, 자기효능감, 직무만족도였다. 자기주도적 학습능력과 직무만족도를 제외한 4가지 종속변수는 메타분석 결과 모두 통계적으로 유의미한 큰 수준의 효과가 확인되었다. 본 연구는 임상간호실습교육을 위해 사용하는 다양한 시뮬레이션 실습방법 중 하나인 역할극을 활용한 간호실습교육의 효과를 확인하였다. 이는 최적의 간호실습교육을 구성하기 위한 구체적인 근거기반자료로 활용될 수 있을 것이다.
날씨와 인기도를 고려한 경북 관광지 추천 알고리즘 개발에 관한 연구
서윤암,김희수,윤상후 한국데이터정보과학회 2022 한국데이터정보과학회지 Vol.33 No.5
The weather has a lot of influence on itinerary decisions. The combination of weather and tourism data can create new values. This study proposes a recommendation algorithm for tourist attractions in North Gyeongsang Province considering the Korean-style tourism climate index and the popularity of tourist attractions according to weather conditions. First, the popularity of tourist attractions was obtained by using the number of reviews, ratings, and blogs provided by Naver. In addition, we obtain optimized popularity scores compared to the number of monthly tourist searches provided by Korea Datalab. Afterward, thermal comfort, wind speed, precipitation, and sunshine hours of tourist attractions are used to generate tourist climate indices. The weather information of tourist attractions was used to predict the weather conditions of the Korea Meteorological Administration's weather station and the latitude and longitude of tourist attractions using the kriging technique. Calculating the Korean-style Tourism Climate Index (KTCI) of tourist attractions through the predicted weather information can quantitatively evaluate the impact of weather conditions on tourism. A tourist recommendation algorithm was developed to reflect the KTCI score in the popularity of the finally optimized tourist attractions in Gyeongbuk. As a result of this study, there is a difference between sunny and cloudy days, but it does not have a significant impact on tourist recommendations and is similar to the ranking considering only popularity. On rainy days, recommendations focused on indoor tourist attractions with less outdoor exposure were prioritized. 날씨는 여행 일정 결정에 많은 영향을 미친다. 날씨와 관광의 데이터 결합은 새로운 가치를 만들어 낼 수 있다. 본 연구는 기상조건에 따른 한국형 관광기후지수와 관광지 인기도를 고려한 경상북도 관광지 추천알고리즘을 제안한다. 먼저 네이버에서 제공하는 관광지별 리뷰 수, 평점, 블로그 수를 이용해 관광지 인기도를 구하였다. 또한, 한국관광 데이터랩에서 제공하는 월별 관광지 검색 건수와 비교하여 최적화된 인기도 점수를 구하였다. 이후 관광지의 관광기후지수를 생성하기 위해 관광지의 열적쾌적성, 풍속, 강수, 일조시간을 이용한다. 관광지의 기상정보는 크리깅 기법을 이용해 기상청의 기상관측소 날씨 데이터와 관광지의 위·경도를 이용하여 관광지의 날씨를 예측하였다. 예측된 기상정보를 통해 관광지의 한국형 관광기후지수 (Korean tourism climate index, KTCI)를 계산하면 관광지의 기상조건이 관광에 미치는 영향을 정량적으로 평가할 수 있다. 최종적으로 최적화한 경상북도 지역 관광지의 인기도에 KTCI 점수를 반영하여 관광지 추천 알고리즘을 개발하였다. 본 연구 결과, 관광지 추천에 있어 맑은 날과 흐린 날은 차이는 있으나 관광지 추천에 큰 영향을 미치지 않으며 인기도만 고려한 순위와 비슷하였다. 비가 내리는 날은 야외 노출 정도가 적은 실내 관광지 위주의 추천이 우선되었다.
Deep Neural Network-Based Concentration Model for Oak Pollen Allergy Warning in South Korea
서윤암,김규랑,조창범,오재원,Tae Hee Kim 대한천식알레르기학회 2020 Allergy, Asthma & Immunology Research Vol.12 No.1
Purpose: Oak is the dominant tree species in Korea. Oak pollen has the highest sensitivity rate among all allergenic tree species in Korea. A deep neural network (DNN)-based estimation model was developed to determine the concentration of oak pollen and overcome the shortcomings of conventional regression models. Methods: The DNN model proposed in this study utilized weather factors as the input and provided pollen concentrations as the output. Weather and pollen concentration data were used from 2007 to 2016 obtained from the Korea Meteorological Administration pollen observation network. Because it is difficult to prevent over-fitting and underestimation by using a DNN model alone, we developed a bootstrap aggregating-type ensemble model. Each of the 30 ensemble members was trained with random sampling at a fixed rate according to the pollen risk grade. To verify the effectiveness of the proposed model, we compared its performance with those of models of regression and support vector regression (SVR) under the same conditions, with respect to the prediction of pollen concentrations, risk levels, and season length. Results: The mean absolute percentage error in the estimated pollen concentrations was 11.18%, 10.37%, and 5.04% for the regression, SVR and DNN models, respectively. The start of the pollen season was estimated to be 20, 22, and 6 days earlier than that predicted by the regression, SVR and DNN models, respectively. Similarly, the end of the pollen season was estimated to be 33, 20, and 9 days later that predicted by the regression, SVR and DNN models, respectively. Conclusions: Overall, the DNN model performed better than the other models. However, the prediction of peak pollen concentrations needs improvement. Improved observation quality with optimization of the DNN model will resolve this issue.