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혈액투석 환자의 약물복용 이행도 및 이행 영향요인: 1차, 2차 의료기관 중심
서연희 ( Yon Hee Seo ),임순옥 ( Sun Og Lim ),현은희 ( Eun Hee Hyeon ),김혜원 ( Hae Won Kim ),엄미란 ( Mi Ran Eom ) 서울대학교 간호과학연구소 2015 간호학의 지평 Vol.12 No.1
Purpose: The purpose of this study was to identify the factors influencing medication adherence in hemodialysis patients among primary medical care and secondary medical care. Methods: A cross-sectional survey design was utilized. Data were collected using questionnaires from 280 hemodialysis patients who had taken prescribed medication regularly as a result of chronic renal failure at primary and secondary medical care in Korea. Data were analyzed using t-test, ANOVA, Pearson correlation coefficients, and multiple regression. Results: There were statistically significant differences in medication adherence according to living area (p=.002), health condition (p<.001), amount of medication (p=.004), inconvenience for taking medication (p<.001), and depression level (p=.001). The mean of medication adherence was 3.72 points. Medication adherence was explained by perceived barrier related to medication taking (β=.338), attitude (β=.250), and depression (β=.132). Conclusion: This study strongly recommended that nursing intervention program to improve medication adherence should be developed and a match control study in improvement of medication adherence for hemodialysis patients needs to be done.
간호대학생의 인공지능에 대한 지식, 인식 및 수용태도가 인공지능 이용의도에 미치는 영향
서연희(Yon Hee Seo),조경아(Kyong Ah Cho) 한국간호연구학회 2022 한국간호연구학회지 Vol.6 No.3
Purpose : Artificial intelligence (AI) has potentials to significantly transform a role of nurses and revolutionise practices of healthcare systems. The aim of this study is to identify influences of AI Knowledge, Perception, and Acceptance Attitude on nursing students’ Intentions to use AI-based healthcare technologies. Methods : The participants included 241 nursing students in Gyeonggi-do and Jeollanam-do, with data collected from 30 May to 30 June 2022 using self-reported questionnaires. The data were analyzed using the SPSS/WIN 25.0 program, with independent t-tests, one-way ANOVA, Pearson’s correlations, and multiple linear regression. Results : The results revealed that AI Knowledge positively correlated with AI perception(r=.40, p<.001), and Acceptance Attitude toward AI(r=.17, p=.007). AI perception positively correlated with AI Acceptance Attitude(r=.53, p <.001), and Intentions to use AI(r=.46, p<.001). AI Acceptance Attitude also positively correlated with Intentions to use AI(r=.52, p<.001). The factors influencing Intentions to use AI-based healthcare technologies included AI Perception(β=.30) and Acceptance Attitude AI Acceptance Attitude(β=.38). The adjusted R2 was .318. Conclusions : It is necessary to develop systematic educational programs on AI technologies and form an organizational culture to improve nursing clinical competency and professionalism for nursing students in healthcare setting.
COVID-19 상황을 경험한 간호대학생의 회복탄력성;사회적지지;커뮤니티 탄력성 및 불안과의 관계
김애정(Ae-Jung,Kim),문진하(Jin-Ha,Moon),서연희(Yon-Hee,Seo) 한국응용과학기술학회 (구.한국유화학회) 2021 한국응용과학기술학회지 Vol.38 No.1
본 연구는 COVID-19 상황을 경험한 간호대학생의 회복탄력성, 사회적지지, 커뮤니티 탄력성 및 불안과의 관계를 알아보고자 시도되었다. 연구대상은 경기도 소재의 Y대학교 대학생 252명을 대상으로 구1조화된 설문지를 이용하여 자료 수집을 하였다. 수집된 자료는 SPSS 25.0 program을 이용하여 기술통계, t-test, ANOVA, Pearson s correlation coefficient로 분석하였다. 연구결과 회복탄력성은 5점 만점에 평균 3.10점, 사회적지지는 5점 만점에 4.22점, 커뮤니티 탄력성은 5점 만점에 3.21점, 불안은 4점 만점에 평균은 2.21점으로 나타났다. 회복탄력성은 사회적지지(r=.32, p<.001)와 커뮤니티 탄력성(r=.18, p=.004)과는 양의 상관관계가 있는 것으로 나타난 반면, 불안과는 음의 상관관계가 있는 것으로 나타났다(r=-64, p<.001). 따라서 대학생들의 회복력을 증진시키기 위해서는 사회적지지와 커뮤니티 탄력성을 강화하고 불안감을 조절하고 대처할 수 있는 프로그램과 현실적인 지원 방안이 필요하다. The purpose of this study was to identify relationship between resilience, social support, community resilience, and anxiety in nursing students. 252 participants were recruited from the university located in Gyeonggi-do and data were collected using self-reported questionnaires. Data were analyzed using the SPSS/WIN 25.0 program, with descriptive statistics x2-test, t-test, ANOVA, Pearson s correlation. The average score were resilience 3.10 out of 5, social support 4.22 out of 5, community resilience 3.21 out of 5, and anxiety 2.21 out of 4. In addition, social support(r=.32, p<.001) and community resilience(r=.18, p=.004) were positively correlated with resilience, but anxiety(r=-64, p<.001) was negatively correlated with resilience in university students. In conclusion, program and comprehensive support system are needed to strengthen social support and community resilience, to control and cope with anxiety in order to improve resilience of the university students.