16 research outputs found

    ๋‚œ๋ฏผ์„ ํ–ฅํ•œ ํ•œ๊ตญ 20๋Œ€์˜ ์ธ์‹ ๋ถ„์„: 2018๋…„ ์ œ์ฃผ๋„ ์˜ˆ๋ฉ˜ ๋‚œ๋ฏผ์— ๋Œ€ํ•œ ์„œ์šธ๋Œ€์ƒ์˜ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ† ๋Œ€๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ(๊ตญ์ œํ˜‘๋ ฅ์ „๊ณต), 2021. 2. ๊น€ํƒœ๊ท .After around 500 Yemeni asylum seekers arrived at Jeju Island seeking refuge in 2018, there was much opposition toward them in South Korea. Youth were most antagonistic compared to other age groups which is a characteristic that contrasts with many Western countries. This paper intends to find answers to why they were most against receiving them and compare Seoul National University (SNU) students opinions with those of other twenty-year-olds. Primary data was collected by conducting surveys to SNU students and a panel from polling agency Macromill Embrain which were later examined via statistical and content analyses. Such first-hand information fills a gap within the literature since Korean youth attitudes specifically toward this issue have been left unaddressed. Moreover, results differ from the consensus during that time that youth were unwelcoming because of job competition worries. The main findings were that participants showed unenthusiasm toward refugees, SNU students had relatively more positive attitudes but with no major distinction, and that the majority have not changed their positions since 2018. Meanwhile intersectionality and deliberative democracy theories were utilized to question whether there was a lack of deliberative opinion-formation and policy-making processes and to highlight the importance of substantive democracy.2018๋…„, 500์—ฌ๋ช…์˜ ์˜ˆ๋ฉ˜ ๋‚œ๋ฏผ์ด ์ œ์ฃผ๋„์— ๋„์ฐฉํ•˜์—ฌ ๋น„ํ˜ธ ์‹ ์ฒญํ•  ๋‹น์‹œ ํ•œ๊ตญ์‚ฌํšŒ์—์„œ ๊ทธ๋“ค์„ ํ–ฅํ•œ ๋ฐ˜๋Œ€ ์—ฌ๋ก ์ด ์ปธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค๋ฅธ ์„œ์–‘ ๊ตญ๊ฐ€์˜ ์ฒญ๋…„๊ณผ ๋‹ฌ๋ฆฌ ํ•œ๊ตญ ๋‚ด ์ „ ์—ฐ๋ น์ธต ์ค‘ ๋‚œ๋ฏผ ์ˆ˜์šฉ์„ ๊ฐ€์žฅ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ๊ทธ๋ฃน์ด ์ Š์€ ์„ธ๋Œ€๋ผ๋Š” ๋‹น์‹œ ์„ค๋ฌธ์กฐ์‚ฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์™œ 20๋Œ€๊ฐ€ ๊ฐ€์žฅ ๋ฐ˜๋Œ€ํ–ˆ๋Š”์ง€ ๊ทธ๋ฆฌ๊ณ  ์„œ์šธ๋Œ€์ƒ์€ ๊ทธ๋“ค๊ณผ ์–ด๋– ํ•œ ์ฐจ์ด์ ๊ณผ ๊ณตํ†ต์ ์„ ๋ณด์ด๋Š”์ง€๋ฅผ ์•Œ์•„๋ณด๋Š”๋ฐ ๋ชฉ์ ์„ ๋‘”๋‹ค. ์„œ์šธ๋Œ€์ƒ ๊ทธ๋ฆฌ๊ณ  ใˆœ๋งˆํฌ๋กœ๋ฐ€ ์— ๋ธŒ๋ ˆ์ธ์˜ 20๋Œ€ ํŒจ๋„์„ ๋Œ€์ƒ์œผ๋กœ ์„ค๋ฌธ์ง€๋ฅผ ๋ฐฐํฌํ•˜์—ฌ ์›์ž๋ฃŒ๋ฅผ ์–ป์€ ํ›„ ํ†ต๊ณ„์  ๊ทธ๋ฆฌ๊ณ  ์ฝ˜ํ…์ธ  ๋ถ„์„์„ ์‹ค์‹œํ–ˆ๋‹ค. ์ฃผ ๊ฒฐ๊ณผ๋กœ๋Š” ์„œ์šธ๋Œ€์ƒ์ด ๋‚œ๋ฏผ ์ˆ˜์šฉ์— ๋Œ€ํ•ด์„œ ํฐ ์ฐจ์ด๋Š” ์—†์—ˆ์ง€๋งŒ ์กฐ๊ธˆ ๋” ๊ธ์ •์ ์ธ ํƒœ๋„๋ฅผ ๋ณด์˜€๋‹ค๋Š” ์ฐจ์ด์ ๊ณผ ๋‘ ์ƒ˜ํ”Œ์˜ ์ฐธ์—ฌ์ž ๋Œ€๋‹ค์ˆ˜๊ฐ€ 2018๋…„ ์ดํ›„๋กœ ์ƒˆ๋กœ์šด ์ •๋ณด์— ์ ‘ํ–ˆ์„ ๊ฒƒ์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ดˆ๊ธฐ/๊ธฐ์กด์˜ ์ž…์žฅ์œผ๋กœ๋ถ€ํ„ฐ ๋ณ€ํ™”๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค๋Š” ๊ณตํ†ต์ ์ด๋‹ค. ๋˜ํ•œ, ๋‘ ์ƒ˜ํ”Œ์˜ ๋Œ€๋‹ค์ˆ˜๋Š” ์ด ์ด์Šˆ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์„ ๊ด€๋ จ ์ •๋ณด๋ฅผ ์ฐพ๋Š” ํ–‰๋™์œผ๋กœ ์ด์–ด๊ฐ€์ง€ ์•Š์•˜์œผ๋ฉฐ ๋‹ค๋ฅธ ์˜๊ฒฌ์„ ๊ฐ–์€ ์‚ฌ๋žŒ๊ณผ ๋Œ€ํ™”๋ฅผ ํ•  ์˜ํ–ฅ์„ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ์ด์™€ ๋”๋ถˆ์–ด, ์œ ์—”๋‚œ๋ฏผ๊ธฐ๊ตฌ์˜ ๋ฏผ๊ฐ„ ๋ถ€๋ฌธ ํ›„์›์œจ์ด ๋†’์„ ๊ฒƒ์ด๋ผ๊ณ  ์˜ˆ์ƒํ•˜์ง€ ๋ชปํ–ˆ๊ณ  ์ฐจ๋ผ๋ฆฌ ๊ตญ๋‚ด ํ›„์›์„ ๋” ๋งŽ์ด ํ•˜๋Š” ๊ฒƒ์ด ๋‚ซ๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ์œผ๋ฉฐ ํ˜„์žฌ ํ•œ๊ตญ์—์„œ ์ง€๋‚ด๊ณ  ์žˆ๋Š” ๋‚œ๋ฏผ๋“ค์— ๋Œ€ํ•ด ์šฐ๋ ค๋ฅผ ํ‘œ์‹œํ–ˆ๋‹ค. ๋ณธ ์กฐ์‚ฌ๋Š” ๋‹น์‹œ์— ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์ดํ›„๋กœ๋„ ๊ฐ„๊ณผ๋˜์—ˆ๋˜ ์ˆ™์˜์ ์ธ ์—ฌ๋ก ํ˜•์„ฑ๊ณผ ์ •์ฑ…์ˆ˜๋ฆฝ ๊ณผ์ •์„ ์ƒํ˜ธ๊ต์ฐจ์„ฑ ์ด๋ก ๊ณผ ์ˆ™์˜ ๋ฏผ์ฃผ์ฃผ์˜ ์ด๋ก ์„ ํ†ตํ•ด ์žฌ๊ณ ํ•˜์˜€๊ณ , ํ–ฅํ›„ ์†Œ์ˆ˜์ง‘๋‹จ์„ ํฌ์šฉํ•˜๋Š” ์‹ค์งˆ์  ๋ฏผ์ฃผ์ฃผ์˜๊ฐ€ ๋ณด์žฅ๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค.Abstract i Table of Contents ii List of Figures iv List of Tables v I. Introduction 1 1.1 Background of South Koreas Response 1 1.2 Research Questions and Objectives 8 II. Literature Review 10 2.1 Securitization 12 2.2 Neoliberalism 14 2.3 Femonationalism 17 2.4 Socio-cultural Psychology 20 2.5 Democracy 22 2.6 Summary 27 III. Theoretical Framework 28 3.1 Group Threat 28 3.2 Intersectionality 30 3.3 Deliberative Democracy 35 IV. Methodology 42 4.1 Selection of Approach 42 4.2 Explanation of Procedure 42 V. Results 46 5.1 Descriptive Statistics 46 5.2 Chi-square Statistics 53 5.3 Content Analysis 56 VI. Discussion 63 6.1 Comparison and Interpretation 63 6.2 Evaluation and Limitations 66 6.3 Significance and Reflections 68 VII. Conclusion 74 References 77 Appendices 89 Appendix 1. Survey Questionnaire (English) 89 Appendix 2. Original Survey Questionnaire (Korean) 97 Appendix 3. Descriptive Statistics of Demographic Data 107 Appendix 4. Categorization of Quotes for Content Analysis 110 Appendix 5. Institutional Review Board (IRB) Approval 130 Abstract in Korean (๊ตญ๋ฌธ ์ดˆ๋ก) 132Maste

    ๋”ฅ๋Ÿฌ๋‹๊ณผ ์ง์› ์˜๊ฒฌ์œผ๋กœ ํŒŒ์•…ํ•œ ์กฐ์ง์˜ ๋ฌดํ˜•๋‚ด๋ถ€์ž์‚ฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021. 2. ์กฐ์„ฑ์ค€.Intangible resources are non-physical firm resources that are critical to a firms success. Among them, we refer to those that directly impact employee experience at work as intangible internal resources (IIR). We attempted to create a comprehensive list of IIR by applying a deep learning model to a large-scale company review dataset. We collected over 1.4 million company reviews written for S&P 500 firms from Glassdoor, one of the largest anonymous company rating and review website. Since Glassdoor reviews represent the collective employee voice, we hypothesized that prominent topics from the collective voice would represent different types of IIR. By applying a deep learning model to the review data, we discovered 24 resource types, among which 15 types such as Atmosphere at Work, Coworkers, and Technological Resources aligned with frameworks from the past literature. We then implemented a keyword extraction model to identify each firms unique characteristics regarding different IIR types. We believe firms could utilize our findings to better understand and manage their strategic resources.๋ฌดํ˜•์ž์‚ฐ์ด๋ž€ ์กฐ์ง์ด ๋ณด์œ ํ•œ ์ž์‚ฐ ์ค‘ ํ˜•ํƒœ๊ฐ€ ์—†๋Š” ์ž์‚ฐ์„ ๋œปํ•˜๋ฉฐ, ์ตœ๊ทผ ๋“ค์–ด ์œ ํ˜•์ž์‚ฐ์ฒ˜๋Ÿผ ๊ธฐ์—…์˜ ์„ฑ๊ณผ์— ๊ธฐ์—ฌํ•˜๋Š” ๋™๋ ฅ ์ค‘ ํ•˜๋‚˜๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์ •์ž‘ ๋ฌด์—‡์ด ๋ฌดํ˜•์ž์‚ฐ์ธ์ง€, ๋ฌดํ˜•์ž์‚ฐ์˜ ์ข…๋ฅ˜์—๋Š” ๋ฌด์—‡์ด ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ํ™œ๋ฐœํ•˜๊ฒŒ ์ง„ํ–‰๋˜์–ด์˜ค์ง€ ์•Š์€ ์‹ค์ •์ด๋‹ค. ํŠนํžˆ ์ง์›์˜ ๊ด€์ ์—์„œ ๋ฐ”๋ผ๋ณธ ๋ฌดํ˜•์ž์‚ฐ, ์ฆ‰ ๋ฌดํ˜•๋‚ด๋ถ€์ž์‚ฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ์—ญ์‹œ ์ด๋ก ์— ๊ธฐ๋ฐ˜ํ•œ ํ”„๋ ˆ์ž„์›Œํฌ ์ด์ƒ์œผ๋กœ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€๋Ÿ‰์˜ ํšŒ์‚ฌ ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ์— ๋”ฅ๋Ÿฌ๋‹์„ ์ ‘๋ชฉ์‹œ์ผœ ๋ฌดํ˜•๋‚ด๋ถ€์ž์‚ฐ์˜ ์ข…๋ฅ˜๋ฅผ ํฌ๊ด„์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ธ๊ณ„ ์ตœ๋Œ€ ํšŒ์‚ฌ ํ‰์  ๋ฐ ๋ฆฌ๋ทฐ ์‚ฌ์ดํŠธ์ธ ๊ธ€๋ž˜์Šค๋„์–ด์—์„œ S&P 500 ํšŒ์‚ฌ์— ๋Œ€ํ•ด ๊ฒŒ์žฌ๋œ 140๋งŒ ๊ฐœ ์ด์ƒ์˜ ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ–ˆ๋‹ค. ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ์ง์›์˜ ๋ชฉ์†Œ๋ฆฌ์—์„œ ์ž์ฃผ ๋“ฑ์žฅํ•˜๋Š” ์ฃผ์ œ๊ฐ€ ๋ฌดํ˜•๋‚ด๋ถ€์ž์‚ฐ์˜ ์ข…๋ฅ˜์™€ ์ผ์น˜ํ•  ๊ฒƒ์ด๋ผ๊ณ  ๊ฐ€์ •ํ•œ ๊ฒƒ์ด๋‹ค. ํ•ด๋‹น ๋ฐ์ดํ„ฐ์— ์–ดํ…์…˜ ๊ธฐ๋ฐ˜์˜ ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ 24๊ฐœ์˜ ์ฃผ์ œ๋ฅผ ์ถ”์ถœํ•˜์˜€๊ณ , ์ด ์ค‘ ์ง์žฅ ๋ถ„์œ„๊ธฐ, ๋™๋ฃŒ, ๊ธฐ์ˆ ์ ์ธ ์ž์› ๋“ฑ 15๊ฐœ์˜ ์ฃผ์ œ๊ฐ€ ๊ธฐ์กด ๋ฌธํ—Œ์—์„œ ์–ธ๊ธ‰๋˜์–ด์˜จ ๋ฌดํ˜•์ž์‚ฐ ์ข…๋ฅ˜์™€ ์ผ์น˜ํ–ˆ์Œ์„ ํ™•์ธํ–ˆ๋‹ค. ์ดํ›„ ํ‚ค์›Œ๋“œ ์ถ”์ถœ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•ด ํšŒ์‚ฌ๋ณ„๋กœ ๋ณด์œ ํ•œ ๊ฐ ๋ฌดํ˜•๋‚ด๋ถ€์ž์‚ฐ์˜ ํŠน์ง•์„ ํŒŒ์•…ํ–ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ์ œ์‹œํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ํšŒ์‚ฌ๋“ค์ด ์ „๋žต์ ์ธ ์ž์‚ฐ์„ ๋ณด๋‹ค ์ž˜ ์ดํ•ดํ•˜๊ณ  ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Abstract i Contents ii List of Tables iv List of Figures v Chapter 1 Introduction 1 Chapter 2 Literature Review 7 2.1 Intangible Resources 7 2.2 Glassdoor 11 2.3 Unsupervised Aspect Extraction Methods 13 2.4 Unsupervised Keyword Extraction Methods 16 Chapter 3 Glassdoor Data 18 3.1 Data Collection 18 3.2 Descriptive Statistics 20 3.3 Text Preprocessing 22 Chapter 4 Unsupervised Methods for IIR and Firm Characteristic Analysis 24 4.1 ABAE Method for IIR Discovery 24 4.2 TF-IDF Method for Firm Characteristic Discovery 28 Chapter 5 Experimental Results 30 5.1 15 IIR Types from ABAE 30 5.2 Unique Firm Characteristics from TF-IDF 39 5.3 Managerial Implications 45 5.4 Evaluation of ABAE 46 Chapter 6 Conclusion 49 Bibliography 51 Appendix 58 ๊ตญ๋ฌธ์ดˆ๋ก 75 ๊ฐ์‚ฌ์˜ ๊ธ€ 76Maste

    ๋ฌด์ฒด์™ธ์ˆœํ™˜ ๊ด€์ƒ๋™๋งฅ์šฐํšŒ์ˆ ์—์„œ ์ˆ˜์ˆ  ์ „ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒ๊ณผ ์•Œ๋ถ€๋ฏผ์˜ ๋น„์œจ์ด ์‚ฌ๋ง๋ฅ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ: ํ›„ํ–ฅ์  ๊ด€์ฐฐ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021. 2. ๊น€ํƒœ๊ฒฝ.Background: Fibrinogen to albumin ratio (FAR) is a recently introduced prognostic marker for patient with coronary artery disease. Present study investigated whether fibrinogen to albumin ratio (FAR) is associated clinical outcome after off-pump coronary artery bypass grafting (OPCAB). Method: We retrospectively reviewed a total of 1759 patients who underwent OPCAB. To evaluate the association between FAR and mortality in OPCAB patients, patients were divided into 4 groups based on FAR quartile. Cox proportional hazards regression analysis was used to assess the association between FAR and all-cause mortality. Propensity score matching was also conducted to compare the cumulative survival rate between higher FAR group and lower FAR group. Results: On multivariable Cox regression analysis, preoperative FAR was an independent risk factor for all-cause mortality after OPCAB (highest quartile HR, 1.933; 95% CI, 1.129-3.310; p=0.016). After propensity score matching, the all-cause mortality was significantly higher in patients in the fourth quartile of FAR compared with those in the remaining quartiles (p=0.03). Conclusion: Higher FAR was associated with increased all-cause mortality after OPCAB. Preoperative FAR could be a prognostic factor for predicting higher mortality after OPCAB.ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๋Š” ์ตœ๊ทผ ๊ด€์ƒ๋™๋งฅ์งˆํ™˜๊ณผ ๊ด€๋ จ ์žˆ๋Š” ์˜ˆํ›„์ธ์ž๋กœ ์†Œ๊ฐœ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฌด์ฒด์™ธ์ˆœํ™˜ ๊ด€์ƒ๋™๋งฅ์šฐํšŒ์ˆ ์„ ๋ฐ›๋Š” ํ™˜์ž์—์„œ ์ˆ˜์ˆ ์ „ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๊ฐ€ ์ˆ˜์ˆ  ํ›„ ์ž„์ƒ์ ์ธ ์˜ˆํ›„์™€ ์—ฐ๊ด€์„ฑ์ด ์žˆ๋Š”์ง€๋ฅผ ์•Œ์•„๋ณด๊ณ ์ž ํ•œ๋‹ค. 2006๋…„ 3์›”๋ถ€ํ„ฐ 2016๋…„ 12์›”๊นŒ์ง€ ๋ฌด์ฒด์™ธ์ˆœํ™˜ ๊ด€์ƒ๋™๋งฅ์šฐํšŒ์ˆ ์„ ๋ฐ›์€ 1759๋ช…์˜ ํ™˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€์œผ๋ฉฐ, ์ˆ˜์ˆ  ์ „ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„์˜ ์‚ฌ๋ถ„์œ„์— ๋”ฐ๋ผ 4๊ตฐ์œผ๋กœ ๋‚˜๋ˆ„์—ˆ๋‹ค. ์ฝ•์ŠคํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„์™€ ์‚ฌ๋ง๋ฅ ์˜ ์—ฐ๊ด€์„ฑ์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ ์„ฑํ–ฅ์ ์ˆ˜ ๋งค์นญ์„ ํ†ตํ•ด ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๊ฐ€ ๋†’์€ ๊ตฐ๊ณผ ๋‚ฎ์€ ๊ตฐ์œผ๋กœ ๋งค์นญํ•˜์—ฌ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„์™€ ์‚ฌ๋ง๋ฅ  ๊ฐ„์˜ ์—ฐ๊ด€์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ฝ•์ŠคํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ์ˆ˜์ˆ ์ „ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๊ฐ€ ๋ฌด์ฒด์™ธ์ˆœํ™˜ ๊ด€์ƒ๋™๋งฅ์šฐํšŒ์ˆ ์„ ๋ฐ›์€ ํ™˜์ž์˜ ์‚ฌ๋ง์— ๋…๋ฆฝ์ ์ธ ์œ„ํ—˜์ธ์ž์ž„์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ ์„ฑํ–ฅ์ ์ˆ˜ ๋งค์นญ ํ›„ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ๊ตฐ๊ณผ ๋‚˜๋จธ์ง€ ๊ตฐ ์‚ฌ์ด์—์„œ๋„ ์œ ์˜ํ•œ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋†’์€ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๋Š” ๋ฌด์ฒด์™ธ์ˆœํ™˜ ๊ด€์ƒ๋™๋งฅ์šฐํšŒ์ˆ  ํ›„์˜ ์‚ฌ๋ง๋ฅ ๊ณผ ์—ฐ๊ด€๋˜์–ด ์žˆ์œผ๋ฉฐ ์ˆ˜์ˆ  ์ „ ํ”ผ๋ธŒ๋ฆฌ๋…ธ๊ฒโˆ™์•Œ๋ถ€๋ฏผ ๋น„๊ฐ€ ๋†’์„ ๊ฒฝ์šฐ ๋ฌด์ฒด์™ธ์ˆœํ™˜ ๊ด€์ƒ๋™๋งฅ์šฐํšŒ์ˆ  ์ดํ›„์˜ ๋†’์€ ์‚ฌ๋ง๋ฅ ์„ ์˜ˆ์ธกํ•˜๋Š” ์˜ˆํ›„์ธ์ž๊ฐ€ ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.Abstract i Contents ii List of tables iii List of figures iv Introduction 01 Methods 03 Results 07 Tables 09 Figures 13 Supplemental tables 17 Supplemental figures 19 Discussion 23 Conclusion 25 References vi Abstract in Korean xiiMaste

    ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ๋ณ„ ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2018. 2. ์ดํฌ์—ฐ.์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ์ •์ฑ…์€ ์ฃผ๊ฑฐ ๋ณต์ง€์  ์ฐจ์›์—์„œ ๊ทธ ์ค‘์š”์„ฑ์ด ํ•œ์ธต ๋” ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋‹ค. ๊ตญ๊ฐ€ ์˜ˆ์‚ฐ์ด ์ƒ๋‹น๋ถ€๋ถ„ ์ฆ๊ฐ€ยทํˆฌ์ž…๋จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ €์†Œ๋“์ธต์˜ ์ฃผ๊ฑฐ์‹คํƒœ๋Š” ๋‹ค๋ฅธ ์†Œ๋“๊ณ„์ธต์— ๋น„ํ•ด ๋งค์šฐ ์—ด์•…ํ•œ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ๊ธˆ ๋‚ด ํ†ตํ•ฉ๊ธ‰์—ฌ ํ˜•ํƒœ๋กœ ์กด์žฌํ•˜๋˜ ์ฃผ๊ฑฐ๊ธ‰์—ฌ๊ฐ€ 2015๋…„๋ถ€ํ„ฐ ๋ถ„๋ฆฌยท์ง€๊ธ‰๋˜์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์ฃผ๊ฑฐ๊ธ‰์—ฌ๋ฅผ ๋ฐ›๋Š” ๋Œ€์ƒ๊ณผ ๊ธˆ์•ก์ด ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํŠนํžˆ ๊ณผ๊ฑฐ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๊ฑด์„ค๋กœ ๋Œ€ํ‘œ๋˜๋˜ ๊ณต๊ธ‰์ž ์œ„์ฃผ ์ฃผ๊ฑฐ์ง€์› ์ •์ฑ…์ด ์ˆ˜์š”์ž ์ค‘์‹ฌ ์ฃผ๊ฑฐ์ง€์› ์ •์ฑ…์œผ๋กœ ๋ณ€ํ™”ํ•˜์—ฌ ์ฃผ๊ฑฐ๋ณต์ง€ ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ์ „ํ™˜๋„ ์„œ์„œํžˆ ์ง„์ „๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ •๋ถ€๊ฐ€ ์‹œํ–‰ ์ค‘์ธ ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ์ •์ฑ…์ด ์ €์†Œ๋“์ธต์˜ ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ ๋ณ€ํ™”์— ๋ฏธ์นœ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๋Š”๋ฐ ๋ชฉ์ ์„ ๋‘์—ˆ๋‹ค. ๊ตญํ† ๊ตํ†ต๋ถ€ ์ฃผ๊ฑฐ์‹คํƒœ์กฐ์‚ฌ๋ฅผ ํ™œ์šฉํ•ด ์‹œ๊ธฐ๋ณ„(2010๋…„, 2016๋…„), ์ง€์—ญ๋ณ„(์„œ์šธ, ๊ฒฝ๊ธฐยท์ธ์ฒœ, ๊ด‘์—ญ์‹œ, ๊ธฐํƒ€์ง€์—ญ)์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ฃผ๊ฑฐ๋งŒ์กฑ๋„๋ฅผ ์ฃผ๊ฑฐํ™˜๊ฒฝ๋งŒ์กฑ๋„์™€ ์ฃผํƒ๋งŒ์กฑ๋„๋กœ ๋ถ„๋ฅ˜ํ•ด ์‚ดํŽด๋ณด๋ฉด, 2010๋…„์— ๋น„ํ•ด 2016๋…„์— ๊ธฐํƒ€์ง€์—ญ ์ฃผ๊ฑฐํ™˜๊ฒฝ๋งŒ์กฑ๋„๋งŒ ์ œ์™ธํ•˜๊ณ  ๋ชจ๋‘ ์ƒ์Šนํ–ˆ๋‹ค. 2010๋…„ ๋Œ€๋น„ 2016๋…„ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์„ ์‚ดํŽด๋ณด๋ฉด ์ €์†Œ๋“์ธต ์ž๊ฐ€ ๊ฐ€๊ตฌ๋Š” PIR์ด ์ฆ๊ฐ€ํ•ด ์ฃผ๊ฑฐ๋น„ ๋ถ€๋‹ด์ด ์ฆ๊ฐ€ํ•œ ๋ฐ˜๋ฉด, ์ €์†Œ๋“์ธต ์ž„์ฐจ๊ฐ€๊ตฌ๋Š” ๊ฐ™์€ ๊ธฐ๊ฐ„ RIR์ด ๊ฐ์†Œํ•ด ์ฃผ๊ฑฐ๋น„ ๋ถ€๋‹ด์ด ์™„ํ™”๋˜์—ˆ๋‹ค. 2010๋…„ ๋Œ€๋น„ 2016๋…„ ์ง€์—ญ๋ณ„ ์ž๊ฐ€๊ฐ€๊ตฌ PIR์€ ์ „ ์ง€์—ญ์—์„œ ์ฆ๊ฐ€ํ–ˆ๊ณ , ํŠนํžˆ ๊ด‘์—ญ์‹œ์—์„œ ํฐ ํญ์œผ๋กœ ์ฆ๊ฐ€ํ•ด ์ฃผ๊ฑฐ๋น„ ๋ถ€๋‹ด์ด ์ฆ๊ฐ€ํ–ˆ๋‹ค. ๊ฐ™์€ ๊ธฐ๊ฐ„ ์ง€์—ญ๋ณ„ ์ž„์ฐจ๊ฐ€๊ตฌ RIR์€ ๊ธฐํƒ€์ง€์—ญ์—์„œ๋งŒ ์ฆ๊ฐ€ํ–ˆ๊ณ , ๋‚˜๋จธ์ง€ ์ง€์—ญ์—์„œ๋Š” ๋ชจ๋‘ ๊ฐ์†Œํ•ด ์ฃผ๊ฑฐ๋น„ ๋ถ€๋‹ด์ด ์™„ํ™”๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ์•ž์„œ ์‚ดํŽด๋ณธ ์‹œ๊ธฐ๋ณ„, ์ง€์—ญ๋ณ„ ๋ณ€ํ™”๊ฐ€ ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ์œผ๋กœ ์ธํ•œ ์˜ํ–ฅ์ธ์ง€๋ฅผ ์‚ดํ”ผ๊ธฐ ์œ„ํ•ด ์ˆ˜๊ธ‰์ง‘๋‹จ๊ณผ ๋น„์ˆ˜๊ธ‰์ง‘๋‹จ์„ ๋‚˜๋ˆ  ๊ฐ€์„ค์„ ์„ธ์›Œ ๋น„๊ตํ–ˆ๋‹ค. ์ €์†Œ๋“์ธต ํ”„๋กœ๊ทธ๋žจ ์ค‘ ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ž„์ฐจ๊ฐ€๊ตฌ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚จ ๊ฒƒ์€ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ž๊ฐ€๊ฐ€๊ตฌ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์€ ๊ตฌ์ž…์ž๊ธˆ ๋Œ€์ถœ์ด ํšจ๊ณผ์ ์ด์—ˆ๋‹ค. ์ฃผํƒ ์ ์œ  ํ˜•ํƒœ ๋ณ€ํ™”์—์„œ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ์ˆ˜ํ˜œ ์ง‘๋‹จ์€ ํ•˜ํ–ฅ์ด๋™์ด ๋‘๋“œ๋Ÿฌ์ง„๋‹ค. ์ด๋Š” ๋ถˆ๊ฐ€ํ”ผํ•˜๊ฒŒ ํ•˜ํ–ฅ ์ด๋™์„ ํ•  ์‹œ, ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์–ด ์ฃผ๊ฑฐ ์•ˆ์ „๋ง์œผ๋กœ ์—ญํ•  ํ•œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ์ฃผ๊ฑฐ๊ธ‰์—ฌ ์ˆ˜๊ธ‰์€ ์†Œ๋“๋ถ„์œ„๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ์ˆ˜ํ˜œ์ง‘๋‹จ ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์ด ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ์ง€์—ญ๋ณ„ ์ž„๋Œ€๋ฃŒ ์ฐจ์ด๋กœ ์ธํ•ด ์ž„๋Œ€๋ฃŒ๊ฐ€ ์ €๋ ดํ•œ ์ง€์—ญ์€ ๊ตฌ์ž…๋Œ€์ถœ ์ง€์›์„ ๋ฐ›์•„ ๋‚ด ์ง‘ ๋งˆ๋ จ์„ ์‹คํ˜„ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ์ž„๋Œ€๋ฃŒ๊ฐ€ ๋†’์€ ์ง€์—ญ์€ ์ „์„ธ๋Œ€์ถœ ์ง€์›์„ ์„ ํ˜ธํ–ˆ๋‹ค. ์ค‘๋ณต์ˆ˜ํ˜œ๋กœ ์ธํ•œ ์ฃผ๊ฑฐ๋งŒ์กฑ๋„ ์ƒ์Šนํšจ๊ณผ๋Š” ํฌ์ง€ ์•Š์•˜์œผ๋‚˜, ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์€ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์— ๊ฑฐ์ฃผํ•˜๋ฉฐ ์ฃผ๊ฑฐ๊ธ‰์—ฌ๋ฅผ ์ˆ˜๊ธ‰ํ•˜๋Š” ์ง‘๋‹จ์ด ํฌ๊ฒŒ ์ƒ์Šนํ–ˆ๋‹ค. ์ด๋Š” ์ฃผ๊ฑฐ๊ธ‰์—ฌ๊ฐ€ ์ž„๋Œ€์ธ ๊ณ„์ขŒ๋กœ ๋ฐ”๋กœ ์ž…๊ธˆ๋˜๊ธฐ์— ์ฃผํƒ๋ฐ”์šฐ์ฒ˜ ์„ฑ๊ฒฉ์„ ๋ค๋‹ค. ์ด ์ค‘๋ณต์ˆ˜ํ˜œ์˜ ๊ฒฝ์šฐ๋Š” ์ฃผ๊ฑฐ๊ธ‰์—ฌ๊ฐ€ ๊ฐ€๊ตฌ์ฃผ์—๊ฒŒ ํ˜„๊ธˆ์œผ๋กœ ์ง€๊ธ‰ํ•˜๋Š” ๊ฒƒ๋ณด๋‹ค ๋ฐ”์šฐ์ฒ˜๋กœ ์ง€๊ธ‰ํ•˜๋Š” ๊ฒƒ์ด ์ ์ ˆํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋„ท์งธ, ์žฅ๊ธฐ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์ด ์ฃผ๊ฑฐ๋งŒ์กฑ๋„๋ฅผ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œ์ผฐ์ง€๋งŒ, ๋‹ค๊ฐ€๊ตฌ์ž„๋Œ€ยท๊ธฐ์กด์ฃผํƒ ์ „์„ธ์ž„๋Œ€๊ฐ€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ๋‹ค์ˆ˜ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์ด ๋…ธํ›„ํ™”ํ•˜์—ฌ ํ–ฅํ›„ ๊ด€๋ฆฌ์— ์‹ฌํ˜ˆ์„ ๊ธฐ์šธ์—ฌ์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ์ˆ˜ํ˜œ์œจ์ด ๋งค์šฐ ๋‚ฎ๋‹ค. ์ €์†Œ๋“์ธต์—๊ฒŒ ๋‹ค์–‘ํ•œ ๊ฒฝ๋กœ๋กœ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•ด ์ˆ˜ํ˜œ์œจ์„ ๋†’์—ฌ์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ์ €์†Œ๋“์ธต ์ž๊ฐ€๊ฐ€๊ตฌ ๋น„์œจ์ด ๋งค์šฐ ๋†’์€ ๊ฒƒ์— ๋น„ํ•ด ์ง€์›์ด ๋ฏธ๋น„ํ•˜๋‹ค. ์ €์†Œ๋“์ธต ์ž๊ฐ€ ๋น„์œจ์€ ๊ณผ๋ฐ˜์„ ์ฐจ์ง€ํ•˜๋ฏ€๋กœ ์ด๋“ค์„ ์œ„ํ•œ ์ง€์›์ฑ…๋„ ํ™œ์„ฑํ™”๋˜์–ด์•ผ ํ•œ๋‹ค. ์…‹์งธ, ์ €์†Œ๋“์ธต ๋‚ด์—์„œ๋„ ๋ถ„์œ„๋‚˜ ์†Œ๋“๊ตฐ์— ๋”ฐ๋ผ ์„ ํ˜ธํ•˜๋Š” ํ”„๋กœ๊ทธ๋žจ์ด ์ƒ์ดํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ํ˜„ํ–‰ ์ผ๊ด„์ ์ธ ๊ธฐ์ค€๋ณด๋‹ค๋Š” ์„ธ๋ถ€ํ™”๋œ ์†Œ๋“๋ณ„ ๊ธฐ์ค€์œผ๋กœ ์ฐจ๋“ฑ์„ ๋‘์–ด์•ผ ํ•œ๋‹ค. ๋„ท์งธ, ์ง€์—ญ๋ณ„๋กœ ์„ ํ˜ธํ•˜๋Š” ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ์ด ์ƒ์ดํ•˜๋ฏ€๋กœ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด ๋งž์ถคํ˜• ์ •์ฑ…์„ ์‹œํ–‰ํ•˜๊ณ  ์ค‘์•™์ •๋ถ€๋Š” ์ง€์ž์ฒด์— ์˜ˆ์‚ฐ์„ ๊ต๋ถ€ํ•˜๋Š” ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์ด ๋ฐ”๋žŒ์งํ•˜๋‹ค. ๋‹ค์„ฏ์งธ, ์˜จ์ •์ฃผ์˜์  ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ์ •์ฑ…์„ ์ง€์–‘ํ•œ๋‹ค. ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ๊ฐ„ ์‚ฐ๋ฐœ์  ์ •์ฑ… ์‹œํ–‰๋ณด๋‹ค๋Š” ์ •์ฑ… ๋ชฉํ‘œ ๊ณต์œ ๋ฅผ ํ†ตํ•ด ์œ ๊ธฐ์  ๊ด€๊ณ„๋ฅผ ์œ ์ง€ํ•ด์•ผ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ €์†Œ๋“์ธต์ด ์„ ํ˜ธํ•˜๊ณ  ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚จ ํ”„๋กœ๊ทธ๋žจ์€ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ์ด๊ธฐ ๋•Œ๋ฌธ์— ์•ž์œผ๋กœ๋„ ๋”์šฑ ์ผ๊ด€๋œ ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ์ง€์› ์ •์ฑ…์„ ์ง€์†์ ์œผ๋กœ ์ ๊ทน ํŽผ์ณ ๋‚˜๊ฐ€์•ผ ํ•  ๊ฒƒ์ด๋‹ค.์ œ1์žฅ ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  4 3. ์—ฐ๊ตฌ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 5 1) ์—ฐ๊ตฌ ๋ฒ”์œ„ 5 2) ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 8 4. ์—ฐ๊ตฌ ํ๋ฆ„๋„ 13 ์ œ2์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 14 1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 14 1) ์ฃผ๊ฑฐ๊ถŒ์— ๋Œ€ํ•œ ๊ฐœ๋…์  ์ •์˜ 14 2) ์ฃผํƒ ๋ฌธ์ œ์— ๋”ฐ๋ฅธ ๊ตญ๊ฐ€ ๊ฐœ์ž… 17 3) ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ์ •์ฑ…์˜ ๋ฐœ๋‹ฌ๊ณผ์ • 19 4) ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ์œ ํ˜• 22 5) ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ ๊ฐœ๋… 27 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 31 1) ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› 31 2) ์ฃผ๊ฑฐ๋งŒ์กฑ๋„ 33 3) ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ 37 3. ์„ ํ–‰์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์„ฑ 38 ์ œ3์žฅ ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ ๋ณ€ํ™” ๋ถ„์„ 39 1. ์‘๋‹ต๊ฐ€๊ตฌ ์ผ๋ฐ˜์  ํŠน์„ฑ 39 2. ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ๋งŒ์กฑ๋„ ๋ณ€ํ™” 41 1) ์‹œ๊ธฐ๋ณ„ ๋ณ€ํ™” 41 2) ์ง€์—ญ๋ณ„ ๋ณ€ํ™” 44 3. ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ ๋ณ€ํ™” 46 1) ์‹œ๊ธฐ๋ณ„ ๋ณ€ํ™” 47 2) ์ง€์—ญ๋ณ„ ๋ณ€ํ™” 50 ์ œ4์žฅ ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ์ฃผ๊ฑฐ๋งŒ์กฑ๋„์™€ ์ฃผ๊ฑฐ์•ˆ์ •์„ฑ ๋น„๊ต 54 1. ์ €์†Œ๋“์ธต ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ์ˆ˜ํ˜œ ๋น„์œจ 54 2. ๋ถ„์œ„๋ณ„, ์ง€์—ญ๋ณ„ ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ๋ถ„ํฌ 55 3. ์ฃผ๊ฑฐ์ง€์› ํ”„๋กœ๊ทธ๋žจ ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ฐ€์„ค ๊ฒ€์ • 59 4. ์†Œ๊ฒฐ 79 ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ์‹œ์‚ฌ์  81 1. ์š”์•ฝ 81 2. ์‹œ์‚ฌ์  83 โ–  ์ฐธ๊ณ ๋ฌธํ—Œ 88 Abstract 93Maste

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