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    EDD ์ ๊ฒ€ํ‘œ ๊ธฐ๋ฐ˜ ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ์ด ํ•œ๊ตญ์ธ EFL ํ•™์Šต์ž๋“ค์˜ ํ† ํ”Œ ๋…๋ฆฝํ˜• ์“ฐ๊ธฐ ๊ณผ์ œ ์ˆ˜ํ–‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ธ๋ฌธ๋Œ€ํ•™ ์˜์–ด์˜๋ฌธํ•™๊ณผ, 2019. 2. ์ด์šฉ์›.์˜์–ด ๊ธ€์“ฐ๊ธฐ ์ˆ˜ํ—˜์ž๋“ค์—๊ฒŒ ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•˜๋ ค๋Š” ๋ชฉ์ ์˜ ์ง„๋‹จ ํ‰๊ฐ€ ์ฒ™๋„ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์€ ์ฃผ๋ชฉ์„ ๋ฐ›์•˜๋˜ ๋ฐ˜๋ฉด (Kim, 2010Knoch, 2009), ์ด๋Ÿฌํ•œ ํ‰๊ฐ€ ์ฒ™๋„์˜ ํƒ€๋‹น์„ฑ๊ณผ ํšจ๊ณผ์„ฑ์„ ํ‰๊ฐ€ํ•ด ๋ณด๋ ค๋Š” ์‹œ๋„๋Š” ์ ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” (1) TOEFL iBT ๋…๋ฆฝํ˜• ๊ธ€์“ฐ๊ธฐ ๊ณผ์ œ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ Kim (2010)์— ์˜ํ•ด ์ฒ˜์Œ ๊ฐœ๋ฐœ๋œ EDD (Empirically-derived Descriptor-based Diagnostic) ์ ๊ฒ€ํ‘œ์˜ ํ•œ๊ตญ EFL ์ƒํ™ฉ์—์„œ์˜ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ๊ณผ ํƒ€๋‹น์„ฑ์„ ๊ฒ€ํ† ํ•˜๊ณ , (2) EDD ์ ๊ฒ€ํ‘œ ๊ธฐ๋ฐ˜์˜ ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ์ด ํ•œ๊ตญ์ธ EFL ํ•™์Šต์ž์˜ ์˜์–ด ๊ธ€์“ฐ๊ธฐ ์‹ค๋ ฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ ๊ฒ€ํ•ด ๋ณด๊ณ , (3) ํ•œ๊ตญ์ธ EFL ํ•™์Šต์ž ๋ฐ ์ฑ„์ ์ž์˜ EDD ์ ๊ฒ€ํ‘œ ๋ฐ ํ”ผ๋“œ๋ฐฑ์— ๋Œ€ํ•œ ์ธ์‹์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—๋Š”, ์ค‘๊ธ‰ ์ˆ˜์ค€์˜ ์ด 53๋ช…์˜ ํ•œ๊ตญ์ธ EFL ํ•™์Šต์ž๊ฐ€ ์ฐธ์—ฌํ–ˆ์œผ๋ฉฐ, ๊ฐ ๊ทธ๋ฃน์˜ ํ‰๊ท ์ด ๋น„์Šทํ•˜๋„๋ก ๋ฐฐ์น˜๊ณ ์‚ฌ (placement test) ์ ์ˆ˜์— ๋”ฐ๋ผ (1) ์ž๊ธฐํ‰๊ฐ€ ๊ทธ๋ฃน (self-evaluation), (2) ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ ๊ทธ๋ฃน (written diagnostic feedback), (3) ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ ๋ฐ ๊ตฌ๋‘ ํ”ผ๋“œ๋ฐฑ ๊ทธ๋ฃน (oral feedback)์— ์ˆœ์„œ๋Œ€๋กœ ๋ฐฐ์น˜๋˜์—ˆ๋‹ค. ์ˆ˜ํ—˜์ž๋“ค์ด ์ฒซ ๋ฒˆ์งธ ๊ธ€์“ฐ๊ธฐ๋Š” EDD ์ ๊ฒ€ํ‘œ ๋ฐ TOEFL iBT ๋…๋ฆฝํ˜• ๊ธ€์“ฐ๊ธฐ ์ฑ„์ ํ‘œ๋กœ ์ฑ„์ ๋˜์—ˆ๊ณ , EDD ์ ๊ฒ€ํ‘œ์˜ ์ฑ„์  ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ ๊ฐœ์ธ๋ณ„ ๋งž์ถค ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ ์„ฑ์ ํ‘œ๊ฐ€ ์ž‘์„ฑ๋˜์—ˆ๋‹ค. ์ผ์ฃผ์ผ ํ›„, ์ž์‹ ์ด ์†ํ•œ ๊ทธ๋ฃน์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์ข…๋ฅ˜์˜ ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ›์•˜๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฒซ ๋ฒˆ์งธ ๊ธ€์“ฐ๊ธฐ๋ฅผ ์ˆ˜์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด์–ด์„œ, ๋‘ ๋ฒˆ์งธ ์‹œํ—˜์„ ์น˜๋ฅธ ํ›„, ์‚ฌํ›„ ์„ค๋ฌธ์กฐ์‚ฌ์— ์‘ํ–ˆ๋‹ค. ์ˆ˜ํ—˜์ž๋“ค์˜ ์ฑ„์  ๋ฐ ์„ค๋ฌธ ๊ฒฐ๊ณผ๋Š” R, MS Excel, ๋ฐ Google Spreadsheet์œผ๋กœ ์ˆ˜์ง‘ ๋ฐ ์ฒ˜๋ฆฌ๋˜์—ˆ๊ณ , SPSS๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‚œ์ด๋„, ๋ณ€๋ณ„๋„, ์‹ ๋ขฐ๋„, ํƒ€๋‹น๋„, ๋ฐ ํšจ๊ณผ์„ฑ์ด ๊ณ„์‚ฐ๋˜์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, EDD ์ ๊ฒ€ํ‘œ๋Š” ํ•œ๊ตญ EFL ์ƒํ™ฉ์—์„œ ์ˆ˜ํ—˜์ž ๊ฐœ๊ฐœ์ธ์˜ ์žฅ์ ๊ณผ ๋‹จ์ ์— ๊ด€ํ•œ ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ, ํƒ€๋‹น์„ฑ ๋ฐ ํšจ๊ณผ์„ฑ ๊ฒ€์ฆ์— ๊ด€ํ•˜์—ฌ๋Š” ์—‡๊ฐˆ๋ฆฐ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ฒซ์งธ, ์ž‘๋ฌธ ๊ธฐ์ˆ  (mechanics) ๋ฐ ๋ฌธ๋ฒ•์ง€์‹(grammatical knowledge)์— ๊ด€๋ จ๋œ ์„ค๋ช…์–ด(descriptors)๋ฅผ ์ œ์™ธํ•˜๊ณ ๋Š” ์ ์ • ์ˆ˜์ค€ ์ด์ƒ์˜ ๋ณ€๋ณ„๋„, ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น๋„๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋‘˜์งธ, ๋ฐ˜๋ณต์ธก์ • ๋ถ„์‚ฐ๋ถ„์„ ๊ฒฐ๊ณผ ํ”ผ๋“œ๋ฐฑ ๋ณ„ ์„ธ ๊ทธ๋ฃน์— ํ†ต๊ณ„์  ์ฐจ์ด๊ฐ€ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•˜์ง€๋งŒ, EDD ์ด์  ๋ฐ ์„ค๋ฌธ ๊ฒฐ๊ณผ๋Š” EDD ์ ๊ฒ€ํ‘œ์— ์˜ํ•œ ํ”ผ๋“œ๋ฐฑ์˜ ํšจ๊ณผ์„ฑ์— ๊ธ์ •์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ EFL ์ƒํ™ฉ์—์„œ EDD ์ ๊ฒ€ํ‘œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ˆ˜ํ—˜์ž๋“ค์—๊ฒŒ ํšจ๊ณผ์ ์ธ ์ง„๋‹จ ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š”, EDD ์ ๊ฒ€ํ‘œ์˜ ์ž‘๋ฌธ ๊ธฐ์ˆ  ๋ฐ ๋ฌธ๋ฒ•์ง€์‹์— ๊ด€ํ•œ ์„ค๋ช…์–ด์˜ ์ถ”๊ฐ€ ์กฐ์‚ฌ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ๋‚œ์ด๋„ ๋ฐ ์„ค๋ฌธ์— ๋‚˜ํƒ€๋‚œ ํ•œ๊ตญ์ธ EFL ํ•™์Šต์ž์˜ ํŠน์„ฑ ๋ฐ ์ฑ„์ ์ž ํ”ผ๋“œ๋ฐฑ์„ ์ฐธ๊ณ ํ•˜์—ฌ EDD ์ ๊ฒ€ํ‘œ๋ฅผ ์ˆ˜์ • ๋ณด์™„ํ•  ํ•„์š”๊ฐ€ ์žˆ์Œ์„ ์‹œ์‚ฌํ•˜์˜€๋‹ค.Diagnostically-oriented rating scales for writing assessment have been investigated as a promising approach to generating diagnostic feedback for test-takers in essay-based writing assessments in recent years (Alderson, 2005Alderson & Huhta, 2005Lee, 2015Kim, 2010Knoch, 2009). However, very little effort has been made thus far to assess the validity of such rating scales in terms of capturing the writers strengths and weaknesses in real testing contexts and to examine its effectiveness in improving the test-takers writing skills. Against this background, the present study aims to: (a) investigate the validity of the Empirically-derived Descriptor-based Diagnostic (EDD) Checklist initially developed by Kim (2010), based on the essays written for independent writing tasks of the TOEFL iBT along with its applicability in the Korean EFL writing assessment contexts, (b) examine the instructional effects of the EDD feedback on Korean EFL learners writing, and (c) observe the general perceptions of Korean EFL learners and raters about the EDD checklist and its diagnostic feedback. A total of 53 Korean EFL learners were assigned to one of three groups according to their placement test scores. Each group received different combinations of diagnostic feedback: (a) self-evaluation (no feedback), (b) the EDD (written diagnostic) feedback, and (c) the EDD (written diagnostic) feedback combined with additional oral explanations. As for the instrument of the writing test experiment, TOEFL independent writing tasks were used. The test-takers writing scores and diagnostic feedback were generated based on the EDD checklist and the rating rubrics for the TOEFL iBT independent writing tasks. The computer programs R, MS Excel, and Google Spreadsheet were used to prepare data for analysis, and SPSS was used to conduct basic psychometric analyses of the data, such as item analysis, reliability, and correlational analyses and also inferential statistical analysis, such as ANOVAs. This study found that the EDD checklist was generally applicable in Korean EFL context in that the diagnostic scheme generated students strengths and weaknesses from a reliable and valid existing writing task. However, its validation and effectiveness showed mixed results. First, discrimination indices, reliability, and validity were generally at moderate to high level, but mechanics and grammatical knowledge dimension scores required more investigation. Second, repeated measures ANOVA proved that there was no significant difference among different feedback groups, but the EDD composite mean scores and students survey result implied that diagnostic feedback group had positive effect on Korean EFL students writing tasks.CHAPTER 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Questions 3 1.3 Organization of the Thesis 4 Chapter 2. Literature Review 6 2.1 Definitions and Theoretical Frameworks for Language Assessment and Diagnostic Language Assessment (DLA) 6 2.1.1 Definition of Language Assessment. 7 2.1.2 Definition and Frameworks of Diagnostic Language Assessment (DLA). 9 2.1.3 Various Approaches to Diagnostic Language Assessment (DLA). 14 2.2 Cognitive Diagnostic Approaches (CDA) to Language Assessment 16 2.2.1 The Empirically-derived Descriptor-based Diagnostic (EDD) Checklist. 22 2.3 Feedback 30 2.3.1 Effective Feedback to Enhance Learning 30 2.3.2 The Effectiveness of Feedback in SLA and L2 writing 32 2.3.3 Feedback Types. 34 CHAPTER 3. Method 37 3.1 Participants 37 3.2 Instruments 42 3.2.1 Writing tests 42 3.2.2 Rating scales. 44 3.2.3 Feedback. 46 3.3 Procedure 49 3.3.1 Writing tests 49 3.3.2 Rating 51 3.4 Data Collection and Processing 52 3.5 Analysis of the Data 55 CHAPTER 4. Results 59 4.1 Descriptive Statistics 59 4.2 Reliability 62 4.3 Item Analysis: Item easiness and discrimination indices. 63 4.4 Criterion Validity: Correlations among test scores 65 4.5 Repeated Measures ANOVAs for the Effectiveness of the EDD Checklist-based Feedback 68 4.5.1 The Effectiveness of Feedback at the Level of Holistic Score (HS) 69 4.5.2 Impact of Feedback at the Level of the EDD Composite Score. 70 4.5.3 Impact of Feedback at the Level of the EDD Dimension Scores 73 4.6 Korean EFL Learners Perceptions of Feedback for Essay Writing 77 4.7 Raters Perceptions of the EDD checklist 84 CHAPTER 5. Discussion 86 5.1 Applicability of the EDD checklist in Korean EFL situation 86 5.2 The Effectiveness of the EDD Checklist-based feedback 90 CHAPTER 6. Conclusion 93 6.1 Conclusions and Implications 93 6.2 Limitations and Suggestions for applying the EDD checklist in Korean EFL context. 95 Reference 99 Appendices 113 ๊ตญ๋ฌธ์ดˆ๋ก 146Maste

    Professional self-concept and organizational commitment of nurses in special hospitals

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    ๊ฐ„ํ˜ธ๊ด€๋ฆฌ์™€ ๊ต์œก์ „๊ณต/์„์‚ฌ๋ณธ ์—ฐ๊ตฌ๋Š” ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…๊ณผ ์กฐ์ง๋ชฐ์ž…์˜ ์ •๋„์™€ ๊ด€๋ จ์„ฑ์„ ํŒŒ์•…ํ•˜์—ฌ, ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…๊ณผ ์กฐ์ง๋ชฐ์ž…์„ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•ด ๋ณด๊ณ , ํšจ๊ณผ์ ์ธ ์ธ์ ์ž์›๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ๊ธฐ์ดˆ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ ์‹œํ–‰๋œ ์„œ์ˆ ์  ์ƒ๊ด€๊ด€๊ณ„ ์—ฐ๊ตฌ์ด๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž๋Š” ์„œ์šธ ๋ฐ ๊ฒฝ๊ธฐ๋„ ์†Œ์žฌ ๊ด€์ ˆยท์ฒ™์ถ” ์ „๋ฌธ๋ณ‘์›์— ๊ทผ๋ฌดํ•˜๋Š” ๊ฐ„ํ˜ธ์‚ฌ 168๋ช…์œผ๋กœ 2011๋…„ 3์›” 28์ผ๋ถ€ํ„ฐ 4์›” 8์ผ๊นŒ์ง€ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด Arthur(1992)๊ฐ€ ๊ฐœ๋ฐœํ•œ ๋„๊ตฌ๋ฅผ ๊น€์ˆ˜์—ฐ(2002)์ด ์ˆ˜์ •ยท๋ณด์™„ํ•œ ๋„๊ตฌ์™€ ์กฐ์ง๋ชฐ์ž…์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด Mowday, Streers & Porter(1979)๊ฐ€ ๊ฐœ๋ฐœํ•œ ๋„๊ตฌ๋ฅผ ๋ฐ•์ง€์›(2002)์ด ์ˆ˜์ •ยท๋ณด์™„ํ•œ ๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SPSS Win 18.0 program์„ ์ด์šฉํ•˜์—ฌ ์„œ์ˆ ์  ํ†ต๊ณ„, t-test, ANOVA, Pearson''s correlation coefficients, ์‚ฌํ›„๊ฒ€์ฆ์œผ๋กœ Scheffรฉ test๋ฅผ ์ด์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1. ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋… ์ •๋„๋Š” 108์  ๋งŒ์ ์— ํ‰๊ท  73.73์ ์ด์—ˆ๊ณ , ํ‰๊ท ํ‰์ ์€ 4์  ๋งŒ์  ์ค‘ 2.76์ ์œผ๋กœ ํ‰๊ท ์ด์ƒ์ด์—ˆ๋‹ค. ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์˜ ํ•˜์œ„์˜์—ญ๋ณ„ โ€˜์ „๋ฌธ์  ์‹ค๋ฌดโ€™๋Š” 44.80์ (ํ‰๊ท ํ‰์  2.83์ ), โ€˜์˜์‚ฌ์†Œํ†ตโ€™์€ 10.89์ (ํ‰๊ท ํ‰์  2.74์ ), โ€˜๋งŒ์กฑ๊ฐโ€™์€ 18.03์ (ํ‰๊ท ํ‰์  2.60์ ) ์ˆœ์œผ๋กœ โ€˜์ „๋ฌธ์  ์‹ค๋ฌดโ€™์— ๋Œ€ํ•œ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์ด ๊ฐ€์žฅ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์˜ โ€˜์ „๋ฌธ์  ์‹ค๋ฌดโ€™์˜ ํ•˜์œ„์˜์—ญ๋ณ„ ์ ์ˆ˜๋Š” โ€˜๊ธฐ์ˆ โ€™ 14.26์ (ํ‰๊ท ํ‰์  2.89์ ), โ€˜์œตํ†ต์„ฑโ€™ 19.86์ (ํ‰๊ท ํ‰์  2.86์ ), โ€˜์ง€๋„๋ ฅโ€™ 14.26์ (ํ‰๊ท ํ‰์  2.70์ )์œผ๋กœ โ€˜๊ธฐ์ˆ โ€™์ด โ€˜์œตํ†ต์„ฑโ€™์ด๋‚˜ โ€˜์ง€๋„๋ ฅโ€™๋ณด๋‹ค ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 2. ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ์˜ ์กฐ์ง๋ชฐ์ž… ์ •๋„๋Š” ์ด 75์  ๋งŒ์ ์— 46.91์ ์ด์—ˆ๊ณ , ํ‰๊ท ํ‰์ ์€ 5์  ๋งŒ์  ์ค‘ 3.13์ ์œผ๋กœ ๋ณดํ†ต์ด์—ˆ๋‹ค. ์กฐ์ง๋ชฐ์ž…์˜ ํ•˜์œ„์˜์—ญ๋ณ„ ์ ์ˆ˜๋Š” โ€˜์• ์ฐฉโ€™ 19.66์ (ํ‰๊ท ํ‰์  3.28์ ), โ€˜๋™์ผ์‹œโ€™ 18.66์ (ํ‰๊ท ํ‰์  3.11์ ), โ€˜๊ทผ์†โ€™ 8.60์ (ํ‰๊ท ํ‰์  2.87์ ) ์ˆœ์œผ๋กœ โ€˜์• ์ฐฉโ€™์ด ๊ฐ€์žฅ ๋†’์•˜๋‹ค. 3. ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…๊ณผ ์กฐ์ง๋ชฐ์ž… ๊ด€๊ณ„๋Š” ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋… ์ •๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ์กฐ์ง๋ชฐ์ž…์ด ๋†’์•˜๋‹ค. ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์˜ ํ•˜์œ„์˜์—ญ์—์„œ๋Š” ๋งŒ์กฑ๊ฐ, ์œตํ†ต์„ฑ, ๊ธฐ์ˆ , ์ง€๋„๋ ฅ ์ˆœ์œผ๋กœ ์ ์ˆ˜๊ฐ€ ๋†’์„์ˆ˜๋ก ์กฐ์ง๋ชฐ์ž…์ด ๋†’์•˜๋‹ค. 4. 35์„ธ ์ด์ƒ์˜ ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ 35์„ธ ๋ฏธ๋งŒ์˜ ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์ด ๋†’์•˜๊ณ (t=-4.644, p<.001), ๋ฏธํ˜ผ์ž๋ณด๋‹ค ๊ธฐํ˜ผ์ž๊ฐ€(t=-4.194, p<.001), ์ข…๊ต๊ฐ€ ์—†๋Š” ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ข…๊ต๊ฐ€ ์žˆ๋Š” ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์ด ๋†’์•˜๋‹ค(t=2.927, p<.01). ๊ต๋Œ€ ๊ทผ๋ฌด์ž๋ณด๋‹ค ๊ณ ์ • ๊ทผ๋ฌด์ž๊ฐ€(t=3.405, p<.01), ์ผ๋ฐ˜๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ฑ…์ž„๊ฐ„ํ˜ธ์‚ฌ ์ด์ƒ์ด ๋” ๋†’์•˜์œผ๋ฉฐ(t=-5.126, p<.001), ์—ฐ๋ด‰ 3000๋งŒ์› ๋ฏธ๋งŒ๋ณด๋‹ค 3000๋งŒ์› ์ด์ƒ์ด ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์ด ๋†’์•˜๋‹ค(t=-4.943, p<.001). ์‚ฌํ›„๊ฒ€์ฆ๊ฒฐ๊ณผ, 5๋…„ ์ด์ƒ์ธ ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ 5๋…„ ๋ฏธ๋งŒ์ธ ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์ด ๋†’์•˜๊ณ (F=14.185, p<.001), ์™ธ๋ž˜ ๊ทผ๋ฌด ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ ์ˆ˜์ˆ ์‹ค ๋ฐ ๋ณ‘๋™ ๊ทผ๋ฌด ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋…์ด ๋†’์•˜๋‹ค(F=3.278, p<.05). 5. 35์„ธ ์ด์ƒ์˜ ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ 35์„ธ ๋ฏธ๋งŒ์˜ ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์กฐ์ง๋ชฐ์ž…์ด ๋†’์•˜๊ณ (t=-2.690, p<.01), ๋ฏธํ˜ผ์ž๋ณด๋‹ค ๊ธฐํ˜ผ์ž๊ฐ€(t=-2.593, p<.05), ์ผ๋ฐ˜๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ฑ…์ž„๊ฐ„ํ˜ธ์‚ฌ ์ด์ƒ์ด ์กฐ์ง๋ชฐ์ž…์ด ๋†’์•˜๋‹ค(t=-2.913, p<.01). 3000๋งŒ์› ๋ฏธ๋งŒ ๋ณด๋‹ค 3000๋งŒ์› ์ด์ƒ์ด(t=-2.201, p<.05), ์ด์ง์˜๋„๊ฐ€ ์žˆ๋Š” ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์ด์ง์˜๋„๊ฐ€ ์—†๋Š” ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ ์กฐ์ง๋ชฐ์ž…์ด ๋†’์•˜๋‹ค(t=-5.224, p<.001). ์‚ฌํ›„๊ฒ€์ฆ๊ฒฐ๊ณผ, ์™ธ๋ž˜ ๊ทผ๋ฌด ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ ์ˆ˜์ˆ ์‹ค ๋ฐ ๋ณ‘๋™ ๊ทผ๋ฌด ๊ฐ„ํ˜ธ์‚ฌ๋ณด๋‹ค ์กฐ์ง๋ชฐ์ž…์ด ๋†’์•˜๋‹ค(F=3.322, p<.05). ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด, ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋… ์ •๋„๋Š” ํ‰๊ท ์ด์ƒ์ด์—ˆ๊ณ  ์กฐ์ง๋ชฐ์ž… ์ •๋„๋Š” ๋ณดํ†ต์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋… ์ •๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ์กฐ์ง๋ชฐ์ž…์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ, ์ „๋ฌธ๋ณ‘์› ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ „๋ฌธ์ง ์ž์•„๊ฐœ๋… ํ–ฅ์ƒ์„ ์œ„ํ•ด ์ž„์ƒ์—์„œ ํ™•์‹ ์„ ๊ฐ€์ง€๊ณ  ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ๊ณผ ์ง€์‹์Šต๋“์„ ์œ„ํ•œ ์ง€์†์ ์ธ ๊ต์œกํ”„๋กœ๊ทธ๋žจ์ด ์ œ๊ณต๋˜์–ด์•ผ ํ•œ๋‹ค.ope

    Noninvasive lipolysis using HIFU(high intensity focused ultrasound)

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    ์˜๊ณตํ•™๊ณผ/์„์‚ฌ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ณ ๊ฐ•๋„ ์ง‘์† ์ดˆ์ŒํŒŒ๋ฅผ ์ด์šฉํ•œ ๋น„์นจ์Šต์ ์ธ ์ง€๋ฐฉ์œตํ•ด์‹œ์ˆ ์„ ํšจ๊ณผ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ์ดˆ์ŒํŒŒ ํŒŒ๋ผ๋ฏธํ„ฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค.ํŠน์ •ํ•œ ์กฐ๊ฑด์—์„œ ์ „๋‹ฌ๋˜๋Š” ์ดˆ์ŒํŒŒ ์—๋„ˆ์ง€๋Š” ์กฐ์ง ๋‚ด์— ์บ๋น„ํ…Œ์ด์…˜ ํšจ๊ณผ๋ฅผ ์œ ๋„ํ•œ๋‹ค. ์บ๋น„ํ…Œ์ด์…˜ ํšจ๊ณผ๋Š” ์งง์€ ์‹œ๊ฐ„ ๋™์•ˆ ์กฐ์ง ๋‚ด์— ๊ตญ์ง€์ ์œผ๋กœ ๋†’์€ ์—๋„ˆ์ง€๋ฅผ ๋ฐœ์ƒ์‹œํ‚จ๋‹ค. ์ด๋ ‡๊ฒŒ ๋ฐœ์ƒํ•œ ์—๋„ˆ์ง€๋Š” ์ด๋ก ์ ์œผ๋กœ ์ตœ๊ณ  5000โ„ƒ์˜ ์˜จ๋„๊นŒ์ง€ ์ด๋ฅผ ์ˆ˜ ์žˆ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ„์™€ ๊ฐ™์ด ์บ๋น„ํ…Œ์ด์…˜ ํšจ๊ณผ๋ฅผ ์ธํ•ด ๋ฐœ์ƒํ•œ ๊ตญ์ง€์ ์ธ ๋†’์€ ์—๋„ˆ์ง€๊ฐ€ ์กฐ์ง ๋‚ด ์ง€๋ฐฉ์˜ ์ง€๋ฐฉ์‚ฐ ๋ถ„์ž๊ฐ„ ๊ณต์œ ๊ฒฐํ•ฉ์— ์˜ํ–ฅ์„ ์ฃผ์–ด ์ €๋ถ„์ž ์ˆ˜์ค€์˜ ๋ฌผ์งˆ๋กœ ๋ถ„ํ•ด๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.์‹คํ—˜์—์„œ ์‚ฌ์šฉ๋œ ๊ณ ๊ฐ•๋„ ์ง‘์† ์ดˆ์ŒํŒŒ ํŠธ๋žœ์Šค๋“€์„œ์˜ ์ดˆ์ ์˜ ํฌ๊ธฐ๋Š” 1mmร—1mmร—10mm ๋‚ด์™ธ์ด๋ฉฐ, ์ž‘์€ ์ดˆ์ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์ง€๋ฐฉ์กฐ์ง์˜ ํ‘œ๋ฉด์— ๊ดด์‚ฌ ๋“ฑ์˜ ๋ถ€์ž‘์šฉ์„ ์œ ๋ฐœํ•˜์ง€ ์•Š๊ณ  ์ดˆ์  ๋ถ€์œ„์˜ ์ง€๋ฐฉ๋งŒ์„ ์„ ํƒ์ ์œผ๋กœ ์œตํ•ดํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ดˆ์ŒํŒŒ์˜ ์—ดํšจ๊ณผ๋ฅผ ๋ฐฐ์ œํ•˜๊ณ  ์บ๋น„ํ…Œ์ด์…˜์˜ ๊ธฐ๊ณ„์ ์ธ ํšจ๊ณผ๋งŒ์œผ๋กœ ์ง€๋ฐฉ์œตํ•ด๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ์ดˆ์ŒํŒŒ ํŠธ๋žœ์Šค๋“€์„œ์˜ ๊ณต์ง„ ์ฃผํŒŒ์ˆ˜, duty cycle, ๋ฐ˜๋ณต ์ฃผ๊ธฐ ๋ฐ ์Œ์••์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์กฐ์ ˆํ•˜์˜€๋‹ค.์ด๋Ÿฌํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์—๋„ˆ์ง€ ๊ธฐ์ค€์œผ๋กœ ๋ณ€ํ™˜ํ•˜์—ฌ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋„๋ก ํŠธ๋žœ์Šค๋“€์„œ์— ๋Œ€ํ•œ calibration์„ ์‹ค์‹œํ•˜์—ฌ ์Œ์••์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์„ ํ–‰ ์—ฐ๊ตฌ๋กœ ์ดˆ์ŒํŒŒ์˜ ์Œ์žฅ๋ถ„ํฌ์™€ ๊ทธ์— ๋”ฐ๋ฅธ ํšจ๊ณผ๋ฅผ ์‹œ๊ฐ์ ์œผ๋กœ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ”Œ๋ผ์Šคํ‹ฑ ๊ธฐ๋ฐ˜์˜ ํŒฌํ…€์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ดˆ์ŒํŒŒ ์บ๋น„ํ…Œ์ด์…˜ ์—๋„ˆ์ง€๋ฅผ ์ด์šฉํ•˜์—ฌ ์ง€๋ฐฉ์œตํ•ด๋ฅผ ์œ ๋„ํ•˜๊ณ  ํ•จ๋Ÿ‰๋ถ„์„์„ ํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ธ์ฒด์˜ ์ง€๋ฐฉ์กฐ์ง๊ณผ ๊ฐ€์žฅ ์œ ์‚ฌํ•œ ์ง€๋ฐฉ์‚ฐ ๊ตฌ์„ฑ์„ ๊ฐ–๋Š” ๋ผ์ง€์˜ ์ง€๋ฐฉ์กฐ์ง์ด ์‹œํŽธ์œผ๋กœ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ํ”ผ๋ถ€ ๋ฐ ์ฃผ๋ณ€์กฐ์ง์— ์†์ƒ์„ ์ž…ํžˆ์ง€ ์•Š๊ณ  ์ง€๋ฐฉ ์œตํ•ด๋ฅผ ์œ ๋„ํ•˜๋Š” ํŽ„์Šค๋ฐ˜๋ณต์ฃผ๊ธฐ, duty cycle ๋“ฑ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ฒฐ์ •ํ•˜๊ณ , ์ด์— ๋”ฐ๋ผ ์ง€๋ฐฉ ์œตํ•ด๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค.ope
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