69 research outputs found

    ๊ณ ์ฒด์ƒ ์œ ๊ธฐ ํ•ฉ์„ฑ๊ณผ ์„ธํฌ๋ง‰ ๋‹จ๋ฐฑ์งˆ์˜ ๋ถ„๋ฆฌ๋ฅผ ์œ„ํ•œ ์˜ค์˜-๋‚˜์ดํŠธ๋กœ๋ฒค์งˆ ์•„๋ฏผ/์•Œ์ฝ”์˜ฌ ์œ ๋„์ฒด๋“ค์˜ ํ•ฉ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2014. 8. ์ด์œค์‹.Photochemistry is one of the unique and useful subdiscipline of chemistry. Photoreactive molecules, the core compounds in photochemistry, are undergone chemical reactions by irradiation of light, while remaining stable under various conditions such as acidic or basic reaction conditions. With such characteristic features, photoreactive molecules have been used as a linker for solid-phase organic synthesis or a photolabile protecting groups. Furthermore, the light with a wavelength above 315 nm does not give severe damages to biomolecules, and thus, the related photochemistry has been applied to chemical biology field. In this thesis, o-nitrobenzyl amine/alcohol derivatives which can absorb UVA light (365 nm) well are synthesized for solid-phase organic synthesis and isolation cell membrane proteins. In the first part, a novel photocleavable linker which contains o-nitrobenzyl amine moiety was effectively synthesized in six steps with 33 % of synthetic yield, without any complex purification steps. Synthesized photocleavable linker could absorb UVA light with wavelength of 330 to 370 nm well, and showed similar or better photocleavage kinetics compared with established photolinkers. Based on these results, synthesized photocleavable linker was successfully applied to solid-phase organic synthesis. Leu-enkephalin amide (H-YGGFL-NH2) was synthesized by using photolinker-coupled polymer supports, with high purity. Acyl-phenylhydrazone of peptide C-terminus was oxidized by photo-oxidation of photocleavable linker, and peptide acid and ester were synthesized by addition of nucleophiles to acyl-phenyldiazene. Glycopeptides-immobilized polymer supports which can be applied to bioassays were prepared by imine formation reaction between glycans with peptides which coupled with polymer supports via photocleavable linker, and the glycopeptides were analyzed by mass spectroscopy analysis after photocleavage. The synthesized peptides on the photocleavable linker coupled polymer supports were analyzed by laser desorption-ionization mass spectroscopy method without using any additional cleavage steps and matrices. In the second part of thesis, isolation method of the cell membrane protein by using o-nitrobenzyl alcohol moiety containing linkers is described. For the isolation of cell membrane protein, linkers which consisted of amine catchable part, photoreactive part, hydrophilic spacer, and tethering part for immobilization were synthesized. As functional group for tethering, azide which can undergo the copper assisted azide-alkyne cycloaddition reaction and biotin which has high affinity with streptavidin were selected. The reactivity of synthesized molecules toward amine and alkyne was confirmed by a model reaction with amino acids and 4-pentynoic acid in solution phase. Copper assisted azide-alkyne reaction underwent between azide labeled lysine with 4-pentynoic acid in solution-phasehowever, it did not undergo between azide-labeled bovine serum albumin with 4-pentynoic coupled polymer supports. Instead of azide-containing molecule, biotin-containing molecule was used for the isolation of proteins. Bovine serum albumin was labeled with biotin-containing molecule, and this labeled protein was bound with streptavidin which immobilized on the beads. And also, cell membrane proteins of Escherichia coli which were labeled with biotin-containing molecule were bound with streptavidin-coated beads. Bound proteins were released from the beads by irradiation of UVA light. Isolated proteins were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and compared with the results of authentic proteins.ABSTRACT i TABLE OF CONTENTS iv LIST OF TABLES x LIST OF FIGURES xi LIST OF SCHEMES xiv LIST OF ABBREVIATIONS xv I. Introduction 2 I. 1. Basics of Photochemistry 3 I. 1. 1. Principles of photochemistry 3 I. 1. 2. Photoreactive molecules 8 I. 2. Photoreactive Linkers and Protecting Groups 13 I. 2. 1. Photoreactive linkers 13 I. 2. 2. Photoreactive protecting groups 18 I. 3. Applications of Photochemistry 23 I. 3. 1. Photochemistry in solid-phase peptide synthesis 23 I. 3. 2. Photochemistry in chemical biology 30 I. 4. Research Objectives 36 II. Experimental Section 38 II. 1. General 39 II. 1. 1. Materials 39 II. 1. 2. Apparatus 41 II. 1. 3. Ninhydrin Color Test (Kaiser Test) 41 II. 1. 4. Fmoc Quantitation 42 II. 2. Synthesis and Applications of Fmoc-3-amino-3-(4,5-dimethoxy-2-nitrophenyl)propionic Acid (Fmoc-PCA Linker) 44 II. 2. 1. Synthesis of Fmoc-PCA linker 44 Synthesis of 3-amino-3-(3,4-dimethoxyphenyl)propionic acid 44 Synthesis of 1-(3,4-dimethoxyphenyl)-3-methoxy-3-oxopropan-1-aminium chloride 45 Synthesis of methyl 3-(3,4-dimethoxyphenyl)-3-(2,2,2-trifluoroacetamido)propanoate 45 Synthesis methyl 3-(4,5-dimethoxy-2-nitrophenyl)-3-(2,2,2-trifluoroacetamido)propanoate 46 Synthesis of Fmoc-PCA linker 47 II. 2. 2. Applications of Fmoc-PCA linker 49 Preparation of Acetylated PCA linker for the measurement of UV-Vis absorbency of PCA linker 49 Photocleavage of Fmoc-Phe-NH2 49 Synthesis of peptide amide with Fmoc-PCA linker coupled resins 50 Synthesis of peptide acid and peptide methyl ester with phenylhydrazine-PCA coupled resins 51 Preparation of H-ฮฒ-Ala-ฮต-ACA-ฮฒ-Ala-ฮต-ACA-PCA-HiCore for glycan immobilization 53 Immobilization of lactose and 3สน-sialyllactose to prepared resins 53 Preparation of additive solutions for direct on-bead laser desorption/ionization-time of flight (DOLDI-TOF) method 54 Loading of peptide-anchored resins onto MALDI plate 55 Analysis of peptide with DOLDI-TOF method 55 Analysis of peptide with DOLDI-TOF method by using various salt additives 56 II. 3. Synthesis and Application of Protein Fishing Molecule (ProFiM) 57 II. 3. 1. Synthesis of ProFiM 57 Synthesis of ethyl 4-(4-acetyl-2-methoxyphenoxy)butanoate 57 Synthesis of ethyl 4-(4-(1-hydroxyethyl)-2-methoxy-5-nitrophen-oxy)butanoate 58 Synthesis of 4-(4-(1-hydroxyethyl)-2-methoxy-5-nitrophenoxy)-butanoic acid 59 Synthesis of ProFiM-azide and ProFiM-biotin 60 II. 3. 2. Applications of ProFiM for isolation of cell membrane proteins 62 Reactivity test of ProFiM-azide toward amine and alkyne with model amino acids 62 Preparation of alkyne-coupled polymer support for click chemistry 62 Capture-and-release performance of ProFiM-azide with model protein 63 Capture-and-release performance of ProFiM-biotin with model protein 64 Isolation of outer cell membrane proteins of E. coli with ProFiM-biotin 65 III. Results and Discussion 66 III. 1. Synthesis and Applications of Fmoc-PCA Linker 67 III. 1. 1. Synthesis of Fmoc-PCA linker 67 III. 1. 2. UV-Vis absorption spectra and photocleavage kinetics of Fmoc-PCA Linker 70 III. 1. 3. Synthesis of peptide amide with Fmoc-PCA linker coupled resins 73 III. 1. 4. Synthesis of peptide acid and peptide methyl ester with phenylhydrazine-PCA coupled resins 78 III. 1. 5. Immobilization of glycans onto PCA linker coupled resins for on-bead assays 84 III. 1. 6. Mass analysis of peptides with Direct On-bead Laser Desorption/Ionization method 86 III. 2. Synthesis and Application of Protein Fishing Molecule (ProFiM) 94 III. 2. 1. Synthesis of ProFiM 96 III. 2. 2. Reactivity test of ProFiM-azide toward amine and alkyne 103 III. 2. 3. Capture-and-release performance of ProFiM-azide and ProFiM-biotin with model protein 107 III. 2. 4. Isolation of cell membrane proteins of E. coli with ProFiM-biotin 111 IV. Conclusions 113 References 116 Abstract in Korean 133Docto

    Improving Time Series Forecasting Performance of Deep Learning Models by Enhancing Dynamic Time Warping based Loss Function

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2023. 2. ์ด๊ฒฝ์‹.๋‹ค์‹œ์  ์‹œ๊ณ„์—ด ์˜ˆ์ธก์€ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•˜๋Š” ๊ธฐ๋ก ๋ฐ์ดํ„ฐ๋ฅผ ํš๋“ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์‚ฐ์—… ๋ฐ ์—ฐ๊ตฌ ๋ถ„์•ผ์—์„œ ๋งค์šฐ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜์–ด ์˜ค๋˜ ์ฃผ์ œ์ด๋‹ค. ํ˜„์žฌ๋Š” ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ์ด์šฉํ•ด ์‹œ๊ณ„์—ด์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ์ธ ์ฃผ๊ธฐ์„ฑ, ์ถ”์„ธ์„ฑ, ๋น„๊ทœ์น™์„ฑ ๋“ฑ์˜ ์‹œ๊ฐ„์  ์—ญํ•™(temporal dynamics)์„ ํ•™์Šตํ•˜๋Š” ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก ์ด ์ผ๋ฐ˜์ ์ด๋‚˜, ๋ชจ๋ธ์˜ ์˜ˆ์ธก ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ํ•™์Šต ๋ฐฉ๋ฒ• ๋ฐ ๋ฐฉํ–ฅ์„ ๊ฒฐ์ •ํ•˜๋Š” ์†์‹คํ•จ์ˆ˜์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง๊นŒ์ง€ ๋งŽ์ง€ ์•Š๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹œ๊ณ„์—ด ์˜ˆ์ธก ์šฉ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์˜ ํ•™์Šต์„ ์œ„ํ•œ ๊ฐœ์„ ๋œ ๋™์  ์‹œ๊ฐ„ ์ •ํ•ฉ (Dynamic Time Warping, DTW) ๊ธฐ๋ฐ˜ ์†์‹คํ•จ์ˆ˜๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋ฏธ๋ถ„ ๊ฐ€๋Šฅํ•œ ๋™์  ์‹œ๊ฐ„ ์ •ํ•ฉ์— ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ๊ฐ€์ค‘ ๋ฐฉ๋ฒ•(Weighted DTW)๊ณผ ์ฃผ๋ณ€ ์‹œ์ ๋“ค์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜๋Š” ๋ชจ์–‘ ๊ธฐ์ˆ ์ž๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•(Shape DTW)๋ฅผ ์ ์šฉํ•˜์—ฌ ๋ชฉํ‘œ ์‹œ๊ณ„์—ด์˜ ๋ชจ์–‘(๋ณ€ํ™”์˜ ํฌ๊ธฐ์™€ ์‹œ์ )์„ ๋”์šฑ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ์˜๋„ํ•˜์˜€๋‹ค. ์ œ์‹œํ•œ ์†์‹คํ•จ์ˆ˜๋ฅผ ์—ฌ๋Ÿฌ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ๊ณผ ์„œ๋กœ ๋‹ค๋ฅธ ํŠน์ง•์„ ๊ฐ–๋Š” ์‹ค์ œ ๋ฐ์ดํ„ฐ ์…‹๋“ค์— ์ ์šฉํ•˜๊ณ  ์œ ํด๋ฆฌ๋“œ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ์†์‹คํ•จ ์ˆ˜ ๋ฐ ๊ธฐ์กด ๋™์  ์‹œ๊ฐ„ ์ •ํ•ฉ ๊ธฐ๋ฐ˜ ์†์‹คํ•จ์ˆ˜์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์˜ˆ์ธก ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์„ ๋ณด์˜€ ๋‹ค. ๋˜ํ•œ ์ •๋Ÿ‰์  ๊ด€์ ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์‹œ๊ณ„์—ด ์˜ˆ์ธก์„ ์—ฌ๋Ÿฌ ๊ด€์ ์—์„œ ํ‰๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ์ •์„ฑ์  ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์ œ์‹œํ•œ ์†์‹คํ•จ์ˆ˜๊ฐ€ ์‹œ๊ณ„์—ด์˜ ๊ธ‰๊ฒฉํ•œ ๋ณ€ํ™”๋ฅผ ๋”์šฑ ์ž˜ ์˜ˆ์ธกํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ ๊ธฐ์กด ์†์‹คํ•จ์ˆ˜๋ฅผ ์ถฉ๋ถ„ํžˆ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Multi-step time series forecasting is a topic that has been studied very actively in various industries and research fields that can obtain historical data that changes over time. Currently, research methodologies that use deep learning models to learn temporal dynamics such as periodicity, trendiness, and irregularity, which are unique characteristics of time series, are common, but there are not many studies on loss functions that evaluate predictions of model and determine learning methods and directions. In this paper, we present an improved dynamic time warping (DTW)-based loss function for learning deep learning models for time series prediction. We intend to apply a distance-based weighting method (Weighted DTW) and a shape descripting 68 method (Shape DTW) that considers the surrounding points together, to a differ entiable DTW to more accurately predict the shape of the target time series (size and timing of change). We apply the proposed loss function to several deep learning models and real-world datasets with different features, and compare it with Eu clidean distance-based loss function and existing DTW-based loss function to show improved predictive performance. In addition, time series prediction was evaluated from various perspectives using various evaluation indicators from a quantitative point of view, and it was confirmed that the loss function presented through qual itative evaluation predicts the rapid change of the time series better, which can sufficiently replace the existing loss function.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ 2 1.2 ์—ฐ๊ตฌ ๋ชฉ์  4 1.3 ๋ฌธ์ œ ์ •์˜ 5 1.4 ๋…ผ๋ฌธ๊ตฌ์„ฑ 6 ์ œ 2 ์žฅ ๋ฐฐ๊ฒฝ ์ด๋ก  ๋ฐ ๊ด€๋ จ ์—ฐ๊ตฌ 7 2.1 ๋ฐฐ๊ฒฝ ์ด๋ก  7 2.1.1 ๋™์  ์‹œ๊ฐ„ ์ •ํ•ฉ (Dynamic Time Wariping) 7 2.2 ๊ด€๋ จ ์—ฐ๊ตฌ 14 2.2.1 ๋‹ค์‹œ์  ์‹œ๊ณ„์—ด ์˜ˆ์ธก๊ณผ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ 14 2.2.2 Transformer์™€ ๋น„(้ž) Transformer ์‹œ๊ณ„์—ด ์˜ˆ์ธก ๋ชจ๋ธ 16 2.2.3 ๋™์  ์‹œ๊ฐ„ ์ •ํ•ฉ ๊ธฐ๋ฐ˜ ์†์‹คํ•จ์ˆ˜ ์—ฐ๊ตฌ 18 ์ œ 3 ์žฅ ๋™์  ์‹œ๊ฐ„ ์ •ํ•ฉ ๊ธฐ๋ฐ˜ ์†์‹คํ•จ์ˆ˜ ๊ฐœ์„  ๊ธฐ๋ฒ• 19 3.1 Soft DTW์™€ DILATE 19 3.2 Weighted SDTW 21 3.3 Shape DILATE 25 ์ œ 4 ์žฅ ์‹คํ—˜ ๊ฒฐ๊ณผ 29 4.1 ๋ชจ๋ธ 29 4.1.1 TCN 29 4.1.2 NBeats 30 4.1.3 DLinear 31 4.2 ๋ฐ์ดํ„ฐ ์…‹ 33 4.2.1 ETT 33 4.2.2 WTH 35 4.2.3 ECL 37 4.2.4 BTC 38 4.3 ์‹คํ—˜ ์„ธํŒ… 41 4.4 ์‹คํ—˜ ๊ฒฐ๊ณผ 43 4.4.1 ์ •๋Ÿ‰์  ํ‰๊ฐ€ 43 4.4.2 ์ •์„ฑ์  ํ‰๊ฐ€ 49 ์ œ 5 ์žฅ ๊ฒฐ๋ก  55 5.1 ๊ฒฐ๋ก  55 5.2 ํ–ฅํ›„ ์—ฐ๊ตฌ 56 ์ฐธ๊ณ ๋ฌธํ—Œ 58 Abstract 68์„

    A Study on Efficiency of a Vertical Axis and Variable-Pitch Floating Wind Turbine

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    This thesis is for the research of generating efficiency of the vertical axis and variable-pitch floating wind turbine. This new concept wind turbine is designed to have variable pitch angles while the rotor blade is rotating with the direction of the wind. For the most efficient wind turbine design, I analyzed output characters from the integration between the blade's variable-pitch range, fixed pitch angle, external force of ocean environment and the wind turbine through a wind tunnel test using 2-dimensional basin as well as the floating wind turbine test using the ocean engineering basin. Also, when evaluating for the mechanical efficiency (disregarding the electric efficiency) of the wind turbine, I performed a mechanical power measuring test of torque and the RPM of the generator's pivot1. ์„œ ๋ก  1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋‚ด์šฉ 4 2. ํ’๋ ฅ๋ฐœ์ „ ์‹œ์Šคํ…œ 2.1 ํ’๋ ฅ๋ฐœ์ „ ์‹œ์Šคํ…œ ๊ฐœ์š” 5 2.2 ์ˆ˜ํ‰์ถ• ๋ฐ ์ˆ˜์ง์ถ• ํ’๋ ฅ๋ฐœ์ „๊ธฐ ๋น„๊ต 7 2.3 ํ’๋ ฅ๋ฐœ์ „ ์—๋„ˆ์ง€ ์ด๋ก  10 3. ๋ชจํ˜• ์„ค๊ณ„ ๋ฐ ์ œ์ž‘ 3.1 2์ฐจ์› ์กฐํŒŒ์ˆ˜์กฐ๋ฅผ ์ด์šฉํ•œ ํ’๋™์‹คํ—˜์šฉ ํ’๋ ฅ๋ฐœ์ „๊ธฐ ํ˜•์ƒ ์„ค๊ณ„ 12 3.1.1 ๋ธ”๋ ˆ์ด๋“œ 13 3.1.2 ์Šคํ† ํผ ์žฅ์น˜ 14 3.1.3 ๋ธ”๋ ˆ์ด๋“œ ์ƒยทํ•˜ํŒ ๋ฐ ์ค‘์‹ฌ์ถ• 16 3.1.4 ์ง€์ง€๋Œ€ 19 3.2 ํ•ด์–‘ ๊ณตํ•™์ˆ˜์กฐ๋ฅผ ์ด์šฉํ•œ ๋ถ€์œ ์‹ ํ’๋ ฅ๋ฐœ์ „ ์‹คํ—˜์šฉ ํ’๋ ฅ๋ฐœ์ „๊ธฐ ํ˜•์ƒ ์„ค๊ณ„ 20 3.2.1 ๋ถ€์œ ์ฒด 21 3.2.2 ์ง€์ง€๋Œ€ 22 4. ๋ชจํ˜•์‹คํ—˜ 4.1 ์‹คํ—˜ ์ธก์ • ๊ธฐ๊ธฐ ๋ฐ ํ’๋™์‹คํ—˜ ํ™˜๊ฒฝ ์กฐ์„ฑ 23 4.1.1 ํ’์† ์ธก์ • ๋ฐ ํ’๋™์‹คํ—˜ 30 4.1.2 ๊ธฐ๊ณ„์  ์ถœ๋ ฅ ์‹คํ—˜ ์žฅ๋น„ 32 4.2 2์ฐจ์› ์กฐํŒŒ์ˆ˜์กฐ๋ฅผ ์ด์šฉํ•œ ํ’๋™์‹คํ—˜ 33 4.3 2์ฐจ์› ์กฐํŒŒ์ˆ˜์กฐ๋ฅผ ์ด์šฉํ•œ ํ’๋™์‹คํ—˜ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 41 4.4 ํ•ด์–‘ ๊ณตํ•™์ˆ˜์กฐ๋ฅผ ์ด์šฉํ•œ ํ’๋ ฅ๋ฐœ์ „ ์‹คํ—˜ 60 4.5 ํ•ด์–‘ ๊ณตํ•™์ˆ˜์กฐ๋ฅผ ์ด์šฉํ•œ ํ’๋ ฅ๋ฐœ์ „ ์‹คํ—˜ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 65 5. ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„๊ณผ์ œ 70 References 71Maste

    Trans-boundary Thoughts: Philosophy of Deconstruction and Escape in Kafka

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    ๋ณธ ๋…ผ๋ฌธ์€ ๋ฐ๋ฆฌ๋‹ค์™€ ๋“ค๋ขฐ์ฆˆ์˜ ์นดํ”„์นด ์ฝ๊ธฐ๋ฅผ ํ†ตํ•˜์—ฌ ํƒˆ๊ฒฝ๊ณ„์˜ ์‚ฌ์œ ๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ ์ž ํ•œ๋‹ค. ๊ฒฝ๊ณ„๋Š” ์–ด๋–ค ๊ฒƒ์˜ ์ž๊ธฐ๋™์ผ์„ฑ์„ ๋ณด์žฅํ•˜๋Š” ์šธํƒ€๋ฆฌ ์ด๋ฉด์„œ ๋”๋Š” ๋‚˜์•„๊ฐ€์ง€ ๋ชปํ•˜๊ฒŒ ๋ง‰๋Š” ํ•œ๊ณ„์ด๋‹ค. ํƒˆ๊ฒฝ๊ณ„์˜ ์‚ฌ์œ ๋Š” ๊ณ ์ฐฉํ™”๋œ ๊ทธ ๊ฒฝ๊ณ„๋ฅผ ๋ฌธ์ œ ์‚ผ์œผ๋ฉฐ,๋ชจ๋“  ๊ฒฝ๊ณ„๋“ค ๋ฐ”๊นฅ์œผ๋กœ์˜ ์™„์ „ํ•œ ์ดˆ์›”์ด ์•„๋‹ˆ๋ผ,๊ฒฝ๊ณ„ ์ž์ฒด๋ฅผ ๊ทผ๊ฑฐ ์ง“๋Š” ์กฐ๊ฑด์— ๋Œ€ํ•œ ์žฌ๊ฒ€ํ† ์ด๊ณ , ๊ฒฝ๊ณ„ ์ด์ „์˜ ๋˜๋Š” ๊ฒฝ๊ณ„ ์•„๋ž˜์˜ ์–ด๋–ค ์—ฐ์†์ ์ธ ์‚ฌํƒœ๋ฅผ ์žฌ๋ฐœ๊ฒฌํ•˜๊ณ ์ž ํ•œ๋‹ค.๋ฐ๋ฆฌ๋‹ค-์นดํ”„์นด๋Š” ๋ฒ•๊ณผ ๋ฌธํ•™์ด๋ผ ๋Š” ์ž๊ธฐ๋™์ผ์  ์ฒด๊ณ„๋“ค ์ž์ฒด๊ฐ€ ์ž์‹ ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๋™์‹œ์— ๋ถˆ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜ ๋Š”์œ ์‚ฌ์ดˆ- ์›”๋ก ์ ์กฐ๊ฑด์œผ๋กœ์„œ๋กœ์—๊ฒŒ์˜์กดํ•˜๊ณ ์žˆ์Œ์„๋“œ๋Ÿฌ๋ƒ„์œผ๋กœ์จ๊ทธ์ฒด ๊ณ„๋“ค ์‚ฌ์ด์˜ ๊ฒฝ๊ณ„๋ฅผ ํ•ด์ฒด์‹œํ‚จ๋‹ค.๋“ค๋ขฐ์ฆˆ-์นดํ”„์นด๋Š” ๋‚ด์žฌ์„ฑ์˜ ์žฅ ์•ˆ์—์„œ ์ดˆ ์›”์ ์ธ ๋ฒ•๊ณผ ์„ ๋ถ„์  ๊ถŒ๋ ฅ์— ํฌ์ฐฉ๋˜์ง€ ์•Š๊ณ  ๊ฒฝ๊ณ„๋“ค์„ ์›€์ง์ด๋ฉฐ ๋Š์ž„์—†์ด ํƒˆ ์ฃผํ•˜๋Š” ์š•๋ง์˜ ์‹ค์žฌ์  ์šด๋™์„ ํ†ตํ•ด์„œ ํƒˆ๊ฒฝ๊ณ„๋ฅผ ์‚ฌ์œ ํ•œ๋‹ค. ๋ฐ๋ฆฌ๋‹ค์™€ ๋“ค๋ขฐ์ฆˆ ๋Š” ์ฒ ํ•™๊ณผ๋ฌธํ•™์‚ฌ์ด์˜์ „ํ†ต์ ์ธ๊ฒฝ๊ณ„๋ฅผ๋„˜์–ด์„œ์นดํ”„์นด๋ฌธํ•™์—์ ‘์†ํ•จ์œผ๋กœ ์จ ์ฒ ํ•™์ ์‚ฌ์œ ์™€๊ธ€์“ฐ๊ธฐ์ž์ฒด๋ฅผ์ƒˆ๋กญ๊ฒŒํ™•์žฅํ•˜๋Š”ํƒˆ๊ฒฝ๊ณ„์˜์‚ฌ์œ ๋ฅผ์‹ค์ฒœํ•˜ ๊ณ  ์žˆ๋‹ค.์ด ๋…ผ๋ฌธ์€2002๋…„๋„ ํ•œ๊ตญํ•™์ˆ ์ง„ํฅ์žฌ๋‹จ์˜ ์ง€์›์— ์˜ํ•˜์—ฌ ์—ฐ๊ตฌ๋˜์—ˆ์Œ(KRF-2002-043-A00046)

    Factors affecting medication adherence in patients taking warfarin

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    ์ค‘ํ™˜์ž ์ „๊ณต/์„์‚ฌ[ํ•œ๊ธ€] ์™€ํŒŒ๋ฆฐ์€ ์•ฝ๋ฌผ ๋ฐ ์Œ์‹ ์ƒํ˜ธ์ž‘์šฉ์ด ํฌ๊ณ  ์ข์€ ์น˜๋ฃŒ๋ฒ”์œ„๋ฅผ ๊ฐ€์ง€๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค๋ฅธ ์•ฝ์— ๋น„ํ•ด Medication Adherence๊ฐ€ ๋”์šฑ ์ค‘์š”ํ•˜๋‹ค. ์™€ํŒŒ๋ฆฐ์˜ Medication Adherence๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋จผ์ € Medication Adherence์˜ ์‹คํƒœ๋ฅผ ๋ถ„์„ํ•˜๊ณ  ๊ทธ ์˜ํ–ฅ์š”์ธ์„ ํŒŒ์•…ํ•ด์•ผํ•œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์™€ํŒŒ๋ฆฐ ๋ณต์šฉํ™˜์ž์˜ Medication Adherence(์ดํ•˜ M.A๋กœ ํ‘œํ˜„ํ•œ๋‹ค.) ์‹คํƒœ ๋ฐ ์˜ํ–ฅ์š”์ธ์„ ๊ทœ๋ช…ํ•˜๊ณ  INR๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ์‹œ๋„ ๋˜์—ˆ๋‹ค.์—ฐ๊ตฌ๋Œ€์ƒ์€ ์„œ์šธ์‹œ ์†Œ์žฌ Y๋Œ€ํ•™ ๋ถ€์†๋ณ‘์› ์‹ฌ์žฅํ˜ˆ๊ด€์„ผํ„ฐ ์™ธ๋ž˜๋ฅผ ๋ฐฉ๋ฌธํ•œ ์™€ํŒŒ๋ฆฐ ๋ณต์šฉํ™˜์ž๋กœ 2007๋…„ 4์›” 9์ผ์—์„œ 2007๋…„ 4์›”30์ผ๊นŒ์ง€ ์„ค๋ฌธ์ง€์— ์‘๋‹ต ๊ฐ€๋Šฅํ•œ 230๋ช…์„ ์ž„์˜ ํ‘œ์ถœํ•˜์—ฌ 1๋…„ ์ด๋‚ด์— ์ž…์›๊ฒฝํ—˜์ด ์žˆ๊ฑฐ๋‚˜ ๋ณต์šฉ๊ธฐ๊ฐ„์ด 6๊ฐœ์›” ๋ฏธ๋งŒ์ธ ๋Œ€์ƒ์ž๋Š” ์ œ์™ธํ•˜์—ฌ ์ตœ์ข… 204๋ช…์„ ์„ ์ •ํ•˜์˜€๋‹ค.Medication Adherence ์‹คํƒœ ๋ฐ ์˜ํ–ฅ์š”์ธ์€ ์„ค๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , INR๊ฒฐ๊ณผ์™€ ์˜ํ–ฅ์š”์ธ ์ค‘ ์ผ๋ถ€๋Š” ์˜๋ฌด๊ธฐ๋ก์„ ํ†ตํ•ด ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SAS 8.2 Version์„ ์ด์šฉํ•˜์—ฌ ํ†ต๊ณ„ ์ฒ˜๋ฆฌํ•˜์˜€์œผ๋ฉฐ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.1. ๋Œ€์ƒ์ž์˜ ์™€ํŒŒ๋ฆฐ ๊ด€๋ จ ์•ฝ๋ฌผ์ง€์‹์€ 10์  ๋งŒ์ ์— ํ‰๊ท  6.73(ยฑ1.81)์ ์ด์—ˆ๊ณ , ๊ทธ ์ค‘ INR๊ณผ ๊ด€๋ จ๋œ 2๊ฐ€์ง€ ํ•ญ๋ชฉ์—์„œ๋Š” 25.98%์™€ 15.69%์˜ ์ •๋‹ต๋ฅ ๋กœ ๋‹ค๋ฅธ ํ•ญ๋ชฉ์— ๋น„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์•˜๋‹ค. ์ž๊ธฐํšจ๋Šฅ๊ฐ์€ 30์  ๋งŒ์ ์— ํ‰๊ท  25.92(ยฑ3.14)๋กœ ์ธก์ •๋˜์—ˆ๋‹ค. ๊ทธ ์ค‘ ๋น„ํƒ€๋ฏผ K๊ฐ€ ํ•จ์œ ๋œ ์Œ์‹์„ ์ผ์ •ํ•˜๊ฒŒ ๋ณต์šฉํ•  ์ž์‹ ์ด ์žˆ๋Š๋ƒ๋Š” ์งˆ๋ฌธ์€ 3์  ๋งŒ์ ์— ํ‰๊ท  1.91(ยฑ0.86)๋กœ 10๊ฐœ ํ•ญ๋ชฉ ์ค‘์—์„œ ์ œ์ผ ๋‚ฎ์•˜๋‹ค.2. INR๋ถ„์„ ๊ฒฐ๊ณผ 1๋…„๊ฐ„ ์ด ์ธก์ •๊ฑด์ˆ˜๋Š” 961๊ฑด์ด์—ˆ๊ณ , ๊ทธ ์ค‘ ์น˜๋ฃŒ๋ฒ”์œ„์— ์†ํ•œ ๋น„์œจ์€ 324๊ฑด์œผ๋กœ 33.71%, ์น˜๋ฃŒ๋ฒ”์œ„ ์•„๋ž˜๋กœ ๋ฒ—์–ด๋‚œ ๋น„์œจ์€ 504๊ฑด์œผ๋กœ 52.45%, ์น˜๋ฃŒ๋ฒ”์œ„ ์œ„๋กœ ๋ฒ—์–ด๋‚œ ๋น„์œจ์€ 133๊ฑด์œผ๋กœ 13.84%์˜€๋‹ค. ๋˜ํ•œ ๊ฐœ๊ฐœ์ธ์˜ ์ ์ •INR๋น„์œจ์˜ ํ‰๊ท ์„ ๊ตฌํ•˜์˜€์„ ๋•Œ, 1๋…„๊ฐ„ ์ธก์ •ํ•œ INR์ด ๋ชจ๋‘ ์น˜๋ฃŒ๋ฒ”์œ„์— ๋“ค์—ˆ์„ ๊ฒฝ์šฐ 100%๋กœ ๋ณธ๋‹ค๋ฉด, ์ ์ •INR๋น„์œจ์˜ ํ‰๊ท ์€ 43.80%(ยฑ20.43)์˜€๋‹ค. ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ฒ—์–ด๋‚œ INR๋น„์œจ์˜ ํ‰๊ท ์„ ๊ตฌํ•˜์˜€์„ ๋•Œ, ์น˜๋ฃŒ๋ฒ”์œ„ ์•„๋ž˜๋กœ ๋ฒ—์–ด๋‚œ ๋น„์œจ์˜ ํ‰๊ท ์€ 59.80%(ยฑ25.70)์˜€๊ณ , ์น˜๋ฃŒ๋ฒ”์œ„ ์œ„๋กœ ๋ฒ—์–ด๋‚œ ๋น„์œจ์˜ ํ‰๊ท ์€ 30.80%(ยฑ16.41)๋กœ ์น˜๋ฃŒ๋ฒ”์œ„ ์•„๋ž˜๋กœ ๋ฒ—์–ด๋‚œ ๋น„์œจ์ด ๋†’์•˜๋‹ค.3. ์™€ํŒŒ๋ฆฐ์˜ M.A๋ฅผ ํšŸ์ˆ˜, ์šฉ๋Ÿ‰, ์‹œ๊ฐ„, ์ฃผ์˜์‚ฌํ•ญ ์ค€์ˆ˜๋กœ ๋ณด์•˜์„ ๋•Œ M.A์˜ ๋น„์œจ์€ 204๋ช…์ค‘ 56๋ช…์œผ๋กœ 27.45%์˜€๊ณ , non Medication Adherence(์ดํ•˜ non M.A๋กœ ํ‘œํ˜„ํ•œ๋‹ค)๋Š” 148๋ช…์œผ๋กœ 72.55%์˜€๋‹ค. ๊ทธ ์ค‘ ์ฃผ์˜์‚ฌํ•ญ์„ ์ œ์™ธํ•œ ํšŸ์ˆ˜, ์šฉ๋Ÿ‰, ์‹œ๊ฐ„์ค€์ˆ˜๋ฅผ M.A๋กœ ๋ณด์•˜์„ ๋•Œ๋Š” M.A๊ฐ€ 46.57%์˜€๊ณ , non M.A๋Š” 53.43%์˜€๋‹ค.4. M.A์˜ ์˜ํ–ฅ์š”์ธ์„ ์‚ดํŽด๋ณด๋ฉด ๋Œ€์ƒ์ž์˜ ์„ฑ๋ณ„, ์—ฐ๋ น, ๊ฒฐํ˜ผ, ์ทจ์—…, ๊ฒฝ์ œ์ƒํƒœ, ๊ต์œก์ˆ˜์ค€, ๊ฐ€์กฑ๊ณผ ๋™๊ฑฐ์œ ๋ฌด ๋“ฑ ์ผ๋ฐ˜์  ํŠน์„ฑ๊ณผ ์งˆํ™˜์˜ ์ข…๋ฅ˜, ๋ณต์šฉ๊ธฐ๊ฐ„, ์•ฝ์˜ ์šฉ๋Ÿ‰, ๋™๋ฐ˜์งˆํ™˜์˜ ์ˆ˜, ํ‰๊ท ๋ฐฉ๋ฌธํšŸ์ˆ˜, ๋ถ€์ž‘์šฉ, ์ •๋ณด์œ ๋ฌด ๋“ฑ ์งˆํ™˜ ๋ฐ ์•ฝ๋ฌผ๊ด€๋ จ ํŠน์„ฑ์€ M.A์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š์•˜๋‹ค. ๋ฐ˜๋ฉด ์•ฝ๋ฌผ๊ด€๋ จ ์ง€์‹์€ M.A์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ์œผ๋ฉฐ, non M.A๊ตฐ๊ณผ ๋น„๊ต ์‹œ M.A๊ตฐ์—์„œ ์•ฝ๋ฌผ๊ด€๋ จ ์ง€์‹์ ์ˆ˜๊ฐ€ ๋†’์•˜๋‹ค(P=0.023). ๋˜ํ•œ ํŒ๋ง‰์ˆ˜์ˆ ๋Œ€์ƒ์ž์—์„œ ์ง‘๋‹จ๊ต์œก์ด ์ง€์‹์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‚ดํŽด๋ณด์•˜์„ ๋•Œ ์ง‘๋‹จ๊ต์œก์„ ๋ฐ›์€ ๊ตฐ์ด ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ์ง€์‹์ ์ˆ˜๊ฐ€ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค(P=0.000). ๋Œ€์ƒ์ž์˜ ์ž๊ธฐํšจ๋Šฅ๊ฐ์€ M.A์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ์„ ์ฃผ์—ˆ์œผ๋ฉฐ, non M.A๊ตฐ๊ณผ ๋น„๊ต ์‹œ M.A๊ตฐ์—์„œ ์ž๊ธฐํšจ๋Šฅ๊ฐ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค(P=0.000).5. M.A์™€ INR์˜ ์—ฐ๊ด€์„ฑ์„ ์‚ดํŽด๋ณด๋ฉด ์‹ฌ๋ฐฉ์„ธ๋™ํ™˜์ž ๊ตฐ์—์„œ๋Š” M.A๊ฐ€ INR์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š์•˜์œผ๋ฉฐ, ํŒ๋ง‰์ˆ˜์ˆ ํ™˜์ž ๊ฒฝ์šฐ INR์ด ์น˜๋ฃŒ๋ฒ”์œ„๋ณด๋‹ค ๋†’์€ ๊ตฐ์—์„œ M.A๊ฐ€ INR์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค(P=0.037).๋ณธ ์—ฐ๊ตฌ๋Š” ์™€ํŒŒ๋ฆฐ ๋ณต์šฉํ™˜์ž์˜ M.A์˜ ์˜ํ–ฅ์š”์ธ์„ ์ผ๋ฐ˜์  ํŠน์„ฑ, ์งˆํ™˜ ๋ฐ ์•ฝ๋ฌผ๊ด€๋ จ์š”์ธ, ์•ฝ๋ฌผ์ง€์‹, ์ž๊ธฐํšจ๋Šฅ๊ฐ์˜ ๋‹ค๊ฐ๋„ ์ธก๋ฉด์—์„œ ๋ถ„์„์„ ์‹œ๋„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์•ฝ๋ฌผ์ง€์‹๊ณผ ์ž๊ธฐํšจ๋Šฅ๊ฐ์ด M.A๊ทธ๋ฃน์—์„œ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์™€ํŒŒ๋ฆฐ์˜ M.A์˜ ์˜ํ–ฅ์š”์ธ์ธ ์•ฝ๋ฌผ์ง€์‹๊ณผ ์ž๊ธฐํšจ๋Šฅ๊ฐ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ์„ ์ œ์–ธํ•œ๋‹ค. [์˜๋ฌธ]Because warfarin interacts sensitively with other medication and food, and has a narrow treatment range, medication adherence (hereinafter referred to as MA) for warfarin is more important than for many other drugs. In order to improve warfarin's MA, the actual conditions of MA need to be analyzed and the factors that affect MA discovered. Accordingly, this paper aims to describe the actual conditions of MA for the patients taking warfarin and its factors, and discover its relationship with INR.Patients taking warfarin who had visited the outpatient ward of the Cardiovascular Center, affiliated with Y University located in Seoul, from April9 to 30, 2007, were selected as research subjects, and a selective sampling of 230 patients who could respond to the survey was carried out. Of those, subjects who had been hospitalized within one year period or taken the drug for less than six months were excluded, and a total of 204 responses were ultimately used.For examining the actual conditions of MA and its factors, a survey was used, and INR results and factors influencing MA were partially examined through medical records. The collected data were statistically analyzed using SAS 8.2 Version, and the results are as follows.First, the subjects' medicinal knowledge regarding warfarin averaged 6.73(1.81) out of a perfect score of 10, and in two of the items regarding INR, the percentage of correct answers were 25.98% and 15.69%, which were low in comparison to other items. Self-efficacy measured an average of 25.92(3.14) out of a perfect score of 30. Among those items, the question, "Are you confident about regularly consuming food that contains vitamin K?" had an average response of 1.91(0.86)out of a perfect score of 3, which was the lowest score among the ten itemsSecond, the results of INR analysis showed that the number of total measurements in one year was 961, and among them, measurements that stayed within treatment range numbered 324, or 33.71% of the total, while those that measured below the treatment range numbered 504, or 52.45%, and those that measured went over the treatment range numbered 133, or 13.84%. In addition, when the average of the appropriate INR for each individual was obtained, if all the measured INR for one year was assumed to be within the treatment range and viewed as 100%, then the average of the appropriate INR was 43.80% (20.43). When the out-of-range INR average was obtained using the same method, the average of the ratio that measured below the treatment range was 59.80% (25.70), and the average of the ratio that measured above the treatment range was 30.80% (16.41), thus marking the below treatment range ratio higher than the above ratio.Third, when MA was viewed in terms of observing frequency, dosage, time, and instructions, the ratio of MA was 56 people among 204, or 27.45%, and non-medication adherence (hereinafter referred to as non-MA) was 148, or 72.55%. Among these factors, when MA was viewed only in terms of observing frequency, dosage, and time, excluding instructions, the ratio of MA was 46.57% and non-MA was 53.43%.Fourth, when factors that affected MA were examined, general characteristics such as subject's gender, age, marital status, occupation, economic status, educational level, and whether or not the subject lived with his or her family, as well as disease and drug related characteristics such as disease type, medication period, drug dosage, number of accompanying diseases, average number of visitations, side-effects, and whether or not there was information none of these factors affected MA. On the other hand, knowledge pertaining to medication influenced MA, and when compared to the non-MA group, the MA group's knowledge pertaining to medication was higher (P=0.023). In addition, when the effect of group education on knowledge was examined in valve surgery subjects, the group that received group education showed a higher knowledge score than the group that did not (P=0.000). The subject's self-efficacy exerted the biggest influence on MA, and when compared to the non-MA group, the MA group's self-efficacy was shown to be higher (P=0.000).Fifth, when MA and INR were examined for connections, in atrial fibrillation patients, MA did not affect INR, and in case of valve surgery patients, MA affected INR in the group that had a higher INR than the treatment range (P=0.037).This paper aimed to analyze factors that affect MA in patients who take warfarin in terms of general characteristics, factors pertaining to disease and medication, knowledge of medication, and self-efficacy. The results show that knowledge of medication and self-efficacy exert significant influence in the MA group. Based on its research, this paper purposes the development of a program for raising knowledge of medication and self-efficacy as factors that affect MA to warfarin.ope

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    Factors affecting the quality of life among the patients with pulmonary artery hypertension

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    ๊ฐ„ํ˜ธํ•™๊ณผ๋ณธ ์—ฐ๊ตฌ๋Š” ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ๊ณผ ๊ด€๋ จ์š”์ธ์„ ํŒŒ์•…ํ•˜์—ฌ ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ์„ ์ฆ์ง„์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๊ธฐ์ดˆ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ณ ์ž ์‹œ๋„๋œ ์„œ์ˆ ์  ์กฐ์‚ฌ์—ฐ๊ตฌ์ด๋‹ค. ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด 'ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž ์‚ถ์˜ ์งˆ ์„ค๋ฌธ์ง€(The Living with Pulmonary Artery Hypertension Questionnaire, LPH)โ€˜๋ฅผ ์ˆ˜์ •ํ•˜๊ณ , ํƒ€๋‹น๋„์™€ ์‹ ๋ขฐ๋„๋ฅผ ๊ฒ€์ฆํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์  ์—ฐ๊ตฌ๋ฅผ ๋ณ‘ํ–‰ํ•˜์˜€๋‹ค. 1๋‹จ๊ณ„์—์„œ๋Š” LPH๋„๊ตฌ๋ฅผ ๋ฒˆ์—ญ ํ›„, 8๋ช…์˜ ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž์—๊ฒŒ โ€˜์‚ถ์˜ ์งˆโ€™์— ๋Œ€ํ•œ ์‹ฌ์ธต๋ฉด๋‹ด์„ ์‹œํ–‰ํ•˜์˜€๊ณ , ๋„์ถœ๋œ ์ฃผ์ œ๋ฅผ LPH๋„๊ตฌ์™€ ๋น„๊ตํ•˜์—ฌ LPH๋ฅผ 1์ฐจ ์ˆ˜์ •ํ•˜์˜€๋‹ค. 1์ฐจ ์ˆ˜์ •๋œ LPH๋„๊ตฌ๋Š” ์ „๋ฌธ๊ฐ€ ๋‚ด์šฉํƒ€๋‹น๋„ ๊ฒ€์ฆ ๋ฐ 4๋ช…์˜ ๋Œ€์ƒ์ž์—๊ฒŒ ์ธ์ง€๋ฉด์ ‘์„ ์‹œํ–‰ํ•˜์—ฌ ๋ฌธํ•ญ์„ ์ตœ์ข… ์ˆ˜์ •ํ•˜์˜€๊ณ , ์ˆ˜์ •๋œ LPH๋„๊ตฌ๋Š” 76๋ช…์˜ ๋Œ€์ƒ์ž์—๊ฒŒ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์—ฌ ํƒ€๋‹น๋„์™€ ์‹ ๋ขฐ๋„๋ฅผ ๊ฒ€์ •ํ•˜์˜€๋‹ค. 2๋‹จ๊ณ„ ์กฐ์‚ฌ์—ฐ๊ตฌ์—์„œ๋Š” 2017๋…„ 4์›” 5์ผ๋ถ€ํ„ฐ 5์›” 31์ผ๊นŒ์ง€ ์„œ์šธ์†Œ์žฌ 1๊ฐœ ๋Œ€ํ•™๋ณ‘์› ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํด๋ฆฌ๋‹‰์„ ๋‚ด์›ํ•œ 138๋ช…์˜ ๋Œ€์ƒ์ž์—๊ฒŒ ์„ค๋ฌธ์กฐ์‚ฌ์™€ ์ „์ž ์˜๋ฌด๊ธฐ๋ก์„ ๋ถ„์„ํ•˜์—ฌ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์ž๋ฃŒ ๋ถ„์„์€ SPSS 23.0ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ํƒ์ƒ‰์  ์š”์ธ๋ถ„์„, ๊ธฐ์ˆ ํ†ต๊ณ„, ํ”ผ์–ด์Šจ ์ƒ๊ด€๋ถ„์„, ์ผ์›๋ฐฐ์น˜๋ถ„์‚ฐ๋ถ„์„, t-๊ฒ€์ •, Mann-Whitney๊ฒ€์ •, Kruskal-Wallis ๊ฒ€์ •, ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. A. 1๋‹จ๊ณ„: ํ•œ๊ตญ์–ดํŒ ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž ์‚ถ์˜ ์งˆ ์ธก์ • ๋„๊ตฌ ํƒ€๋‹น๋„์™€ ์‹ ๋ขฐ๋„ 1. ์ตœ์ข… ์ˆ˜์ •๋œ ํ•œ๊ตญ์–ดํŒ LPH๋„๊ตฌ๋Š” 28๋ฌธํ•ญ์˜ 6์  Likert ์ฒ™๋„๋กœ ๊ตฌ์„ฑ๋˜์—ˆ์œผ๋ฉฐ, ํƒ์ƒ‰์  ์š”์ธ๋ถ„์„ ๊ฒฐ๊ณผ ์‹ ์ฒด์  ์˜์—ญ(13๋ฌธํ•ญ), ์ •์„œ์  ์˜์—ญ(8๋ฌธํ•ญ), ๋ถ€ ์ฆ์ƒ ์˜์—ญ(4๋ฌธํ•ญ)์œผ๋กœ ๋ถ„๋ฅ˜๋˜์—ˆ์œผ๋ฉฐ, ํŠน์ •์š”์ธ์œผ๋กœ ๋ถ„๋ฅ˜๋˜์ง€ ์•Š๋Š” 3๋ฌธํ•ญ์€ ๊ธฐํƒ€๋กœ ๋ช…๋ช…ํ•˜์˜€๋‹ค. 2. ํ•œ๊ตญ์–ดํŒ LPH๋„๊ตฌ์˜ ์‹ ๋ขฐ๋„๋Š” Cronbachโ€™s alpha=.96์ด์—ˆ๋‹ค. SF-12(r=-.862, p<.001), VAS(r=-.775, p<.001), ์‹ ์ฒด๊ธฐ๋Šฅ(r=-.862, p<.001), WHO ๊ธฐ๋Šฅ๋ถ„๋ฅ˜(r=-.862, p<.001)๊ณผ ์œ ์˜ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ ์ค€๊ฑฐ ํƒ€๋‹น๋„๋ฅผ ์ž…์ฆํ•˜์˜€๋‹ค. B. 2๋‹จ๊ณ„: ์‚ถ์˜ ์งˆ ๊ด€๋ จ ์š”์ธ 1. ์ผ๋ฐ˜์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ์€ SF-12๋Š” ํ‰๊ท  61.97ยฑ20.05์ , VAS๋Š” ํ‰๊ท  59.73ยฑ20.64์ ์ด์—ˆ๊ณ , ํ•œ๊ตญ์–ดํŒ LPH๋กœ ์ธก์ •๋œ ์งˆ๋ณ‘ํŠน์ด์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ์€ ํ‰๊ท  59.10ยฑ33.41์ ์ด์—ˆ๋‹ค. 2. ์ผ๋ฐ˜์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์‚ถ์˜ ์งˆ์€ ๊ณ ์กธ์ด๋‚˜ ๋Œ€์กธ์ด ์ค‘์กธ๋ณด๋‹ค(p<.001), ์ง์žฅ์ด ์žˆ๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋ฌด์ง๋ณด๋‹ค(SF-12: p=.027, LPH: p=.005), ์Œ์ฃผ(SF-12: p=.031, LPH: p=.024)์™€ ์šด๋™์„ ํ•˜๋Š” ๊ฒฝ์šฐ(p<.001)๊ฐ€ ์‚ถ์˜ ์งˆ์ด ๋†’์•˜๋‹ค. 3. ์ž„์ƒ์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์‚ถ์˜ ์งˆ์€ WHO ๊ธฐ๋Šฅ๋ถ„๋ฅ˜์—์„œ ๊ธฐ๋Šฅ์ด ๋‚ฎ๊ณ (p<.001), ์šฐ์‹ฌ์‹ค ์ˆ˜์ถ•๊ธฐ ์••๋ ฅ์ด ๋†’๊ณ (SF-12: p=.032, LPH: p=.003), 6๋ถ„ ๋ณดํ–‰๊ฒ€์‚ฌ์˜ ์ด ๋ณดํ–‰๊ฑฐ๋ฆฌ๊ฐ€ ์งง๊ณ (SF-12: p<.001, LPH: p=.007), ์‚ฐ์†Œํ˜ธํก๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜๊ณ (p<.001), ๋ณ‘์šฉ์•ฝ๋ฌผ ์š”๋ฒ•์„ ๋ฐ›๋Š” ๊ฒฝ์šฐ(SF-12: p=.003, LPH: p=.021) ์‚ถ์˜ ์งˆ์ด ๋‚ฎ์•˜๋‹ค. 4. ํ•œ๊ตญ์–ดํŒ LPH๋กœ ์ธก์ •๋œ ์งˆ๋ณ‘ํŠน์ด์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ, ์ผ๋ฐ˜์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ(SF-12, VAS), ์‹ ์ฒด๊ธฐ๋Šฅ(KASI), ์‹ ์ฒด์ฆ์ƒ, ์šฐ์šธ, ๋ถˆ์•ˆ, ์‚ฌํšŒ์  ์ง€์ง€๋Š” ๋ณ€์ˆ˜ ๊ฐ„ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ(p<.001)์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์˜๋ฃŒ์ธ ์ง€์ง€๋Š” ์šฐ์šธ(p=.046), ์‚ฌํšŒ์  ์ง€์ง€(p=.014)์™€ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. 5. ์ผ๋ฐ˜์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์œผ๋กœ ์‹ ์ฒด๊ธฐ๋Šฅ(p<.001), ์‹ ์ฒด์ฆ์ƒ(p<.024), ๋ถˆ์•ˆ(p<.001)์ด ๋ฐํ˜€์กŒ์œผ๋ฉฐ, 79.7%์˜ ์„ค๋ช…๋ ฅ์„ ๋ณด์˜€๋‹ค. 6. ์งˆ๋ณ‘ํŠน์ด์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์œผ๋กœ ์‹ ์ฒด๊ธฐ๋Šฅ(p<.001), ์‹ ์ฒด์ฆ์ƒ(p<.001), ๋ถˆ์•ˆ(p<.008)์ด ๋ฐํ˜€์กŒ์œผ๋ฉฐ, ๋ณธ ํšŒ๊ท€๋ชจํ˜•์€ ์งˆ๋ณ‘ํŠน์ด์  ๊ฑด๊ฐ•๊ด€๋ จ ์‚ถ์˜ ์งˆ์— 83.3%์˜ ์„ค๋ช…๋ ฅ์„ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ํ๋™๋งฅ๊ณ ํ˜ˆ์•• ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ์„ ์ฆ์ง„์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์‹ ์ฒด๊ธฐ๋Šฅ, ์‹ ์ฒด์ฆ์ƒ, ๋ถˆ์•ˆ์— ๋Œ€ํ•œ ๊ฐ„ํ˜ธํ•™์  ์ค‘์žฌ๋ฅผ ์ œ์–ธํ•œ๋‹ค.open๋ฐ•

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ฒด์œก๊ต์œก๊ณผ, 2015. 8. ๊น€์—ฐ์ˆ˜.๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ 12์ฃผ๊ฐ„ ICT๋ฅผ ํ™œ์šฉํ•œ ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์ด ๋น„๋งŒ ์ง์žฅ์ธ์˜ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ ๋ฐ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ทœ๋ช…ํ•˜๋Š”๋ฐ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” 2013๋…„ 5์›”๋ถ€ํ„ฐ 8์›”๊นŒ์ง€ ๊ตญ๋‚ด S์‚ฌ์—์„œ ๊ทผ๋ฌดํ•˜๋Š” ๋‚จ์„ฑ ์‚ฌ๋ฌด์ง ์ข…์‚ฌ์ž ์ค‘ ์ตœ์†Œ 3๊ฐœ์›” ์ด์ƒ ์šด๋™์„ ํ•˜์ง€ ์•Š์•˜์œผ๋ฉฐ, ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜(BMI) 25kg/mยฒ์ด์ƒ์˜ ๋น„๋งŒ ์ง์žฅ์ธ 52๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ž๋ฅผ ๋ชจ์ง‘ํ•œ ํ›„ ๊ฐœ์ธ์˜ ์„ ํ˜ธ์— ๋”ฐ๋ผ ๊ฐ๊ฐ ICT ๊ทธ๋ฃน(IG, n=32)๊ณผ Hospital ๊ทธ๋ฃน(HG, n=20)์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์‚ฌ์ „๊ณผ ์‚ฌํ›„์— ๊ฑธ์ณ ๋Œ€์ƒ์ž์˜ ์‹ ์ฒด์กฐ์„ฑ, ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ, ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์„ ์ธก์ •ํ•˜์˜€์œผ๋ฉฐ, ์‚ฌ์ „์— ์ธก์ •๋œ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ ์ˆ˜์ค€์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐœ์ธ์—๊ฒŒ ๋งž๋Š” ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์„ ์ œ๊ณตํ•˜์˜€๋‹ค. ICT ๊ทธ๋ฃน(IG)์€ 12์ฃผ๊ฐ„ ์Šค๋งˆํŠธํฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ํ†ตํ•˜์—ฌ ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์„ ์ œ๊ณต ๋ฐ›์•˜๋‹ค. Hospital ๊ทธ๋ฃน(HG)์€ 3์ฃผ๋งˆ๋‹ค ๋ณ‘์›์— ๋‚ด์›ํ•˜์—ฌ ์šด๋™์ฒ˜๋ฐฉ์‚ฌ์™€์˜ ์ƒ๋‹ด์„ ํ†ตํ•ด ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์„ ์ œ๊ณต ๋ฐ›์•˜์œผ๋ฉฐ, ๊ฐœ์ธ์˜ ์Šค์ผ€์ค„์„ ๊ณ ๋ คํ•˜์—ฌ ์ž์œ ๋กญ๊ฒŒ ์šด๋™์„ ์ˆ˜ํ–‰ํ•  ๊ฒƒ์„ ๊ถŒ์žฅ๋ฐ›์•˜๋‹ค. 12์ฃผ๊ฐ„ ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์„ ์ œ๊ณตํ•œ ํ›„ ์ถœ์„๋ฅ ์ด 80% ์ด์ƒ์ด๋ฉฐ ์‚ฌ์ „ยท์‚ฌํ›„ ๊ฒฐ์ธก๊ฐ’์ด ์—†๋Š” ๋Œ€์ƒ์ž๋ฅผ ๋ฌด์ž‘์œ„ ๋ฐฐ์ •๊ณผ ๋™์งˆ์„ฑ ๊ฒ€์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์—ฌ IG 15๋ช…๊ณผ HG 15๋ช…, ์ด 30๋ช…์„ ์ตœ์ข… ๋Œ€์ƒ์ž๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ธก์ •๋œ ๋ชจ๋“  ๋ณ€์ธ๋“ค์˜ ๊ฐ’์€ SPSS for Windows version 20.0์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ๋ฃน ๊ฐ„์˜ ์ฐจ์ด๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ Independent sample t-test๋ฅผ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ, ๊ทธ๋ฃน ๊ฐ„์˜ ์ฐจ์ด๊ฒ€์ฆ์„ ์œ„ํ•˜์—ฌ Two way repeated ANOVA๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์‹ ์ฒด์กฐ์„ฑ์˜ ๊ฒฝ์šฐ, ICT๋ฅผ ํ™œ์šฉํ•œ ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์ด ์ฒด์ค‘, ์ฒด์ง€๋ฐฉ๋Ÿ‰, ์ฒด์ง€๋ฐฉ๋ฅ , BMI, WHR์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ์˜ ๊ฒฝ์šฐ, ๊ทผ์ง€๊ตฌ๋ ฅ, ์œ ์—ฐ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š”๋ฐ ํšจ๊ณผ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์˜ ๊ฒฝ์šฐ, ํ—ˆ๋ฆฌ๋‘˜๋ ˆ, ์ค‘์„ฑ์ง€๋ฐฉ, HDL-C, ์ด์™„๊ธฐ ํ˜ˆ์••์„ ๊ฐœ์„ ํ•˜๋Š”๋ฐ ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ICT๋ฅผ ํ™œ์šฉํ•œ ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ์ด ๋น„๋งŒ ์ง์žฅ์ธ์˜ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ ๋ฐ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์„ ๊ด€๋ฆฌํ•˜๋Š”๋ฐ ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.๋ชฉ ์ฐจ โ… . ์„œ ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 3. ์—ฐ๊ตฌ์˜ ๊ฐ€์„ค 4 4. ์—ฐ๊ตฌ์˜ ์ œํ•œ์  4 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 6 1. ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ (ICT) 6 1) ICT ํ™œ์šฉ๊ต์œก 6 2) ICT ํ™œ์šฉ๊ต์œก์˜ ํ•„์š”์„ฑ 6 2. ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ 7 1) ์‹ ์ฒด์กฐ์„ฑ 7 2) ๊ทผ ๋ ฅ 8 3) ๊ทผ์ง€๊ตฌ๋ ฅ 8 4) ์œ ์—ฐ์„ฑ 8 5) ์‹ฌํ์ง€๊ตฌ๋ ฅ 8 3. ๋น„ ๋งŒ 9 1) ๋น„๋งŒ๊ณผ ์šด๋™ 10 2) ๋น„๋งŒ๊ณผ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ 11 4. ๋Œ€์‚ฌ์ฆํ›„๊ตฐ 11 1) ๋Œ€์‚ฌ์ฆํ›„๊ตฐ๊ณผ ์šด๋™ 12 2) ๋Œ€์‚ฌ์ฆํ›„๊ตฐ๊ณผ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ 13 โ…ข. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 15 1. ์—ฐ๊ตฌ๋Œ€์ƒ 15 2. ์—ฐ๊ตฌ์„ค๊ณ„ 15 3. ์ธก์ •ํ•ญ๋ชฉ ๋ฐ ๋„๊ตฌ 17 4. ์ธก์ •๋ฐฉ๋ฒ• 18 1) ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ 18 2) ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ 19 5. ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ 20 6. ์ž๋ฃŒ๋ถ„์„ 21 โ…ฅ. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 22 1. ์‹ ์ฒด์กฐ์„ฑ์˜ ๋ณ€ํ™” 22 1) IG์™€ HG์˜ ์‹ ์ฒด์กฐ์„ฑ์˜ ์ฐจ์ด 22 2) IG์™€ HG์˜ ์‹ ์ฒด์กฐ์„ฑ์˜ ๋ณ€ํ™” 23 2. ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ์˜ ๋ณ€ํ™” 29 1) IG์™€ HG์˜ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ์˜ ์ฐจ์ด 29 2) IG์™€ HG์˜ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ์˜ ๋ณ€ํ™” 30 3. ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์˜ ๋ณ€ํ™” 34 1) IG์™€ HG์˜ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์˜ ์ฐจ์ด 34 2) IG์™€ HG์˜ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์˜ ๋ณ€ํ™” 35 โ…ค. ๋…ผ ์˜ 41 1. ์‹ ์ฒด์กฐ์„ฑ 41 2. ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ 43 3. ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ 45 โ…ฅ . ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 48 1. ๊ฒฐ ๋ก  48 2. ์ œ ์–ธ 49 ์ฐธ๊ณ  ๋ฌธํ—Œ 50 Abstract 60 ํ‘œ ๋ชฉ ์ฐจ ํ‘œ 1. ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ 14 ํ‘œ 2. ์ธก์ •ํ•ญ๋ชฉ ๋ฐ ๋„๊ตฌ 17 ํ‘œ 3. ICT ๊ทธ๋ฃน๊ณผ Hospital ๊ทธ๋ฃน์˜ ์‹ ์ฒด์กฐ์„ฑ์˜ ์ฐจ์ด 22 ํ‘œ 4. ์ฒด์ค‘(kg)์˜ ๋ณ€ํ™” 23 ํ‘œ 5. ๊ณจ๊ฒฉ๊ทผ๋Ÿ‰(kg)์˜ ๋ณ€ํ™” 24 ํ‘œ 6. ์ฒด์ง€๋ฐฉ๋Ÿ‰(kg)์˜ ๋ณ€ํ™” 25 ํ‘œ 7. ์ฒด์ง€๋ฐฉ๋ฅ (%)์˜ ๋ณ€ํ™” 26 ํ‘œ 8. BMI(kg/m2)์˜ ๋ณ€ํ™” 27 ํ‘œ 9. WHR์˜ ๋ณ€ํ™” 28 ํ‘œ 10. ICT ๊ทธ๋ฃน๊ณผ Hospital ๊ทธ๋ฃน์˜ ๊ฑด๊ฐ•๊ด€๋ จ ์ฒด๋ ฅ์˜ ์ฐจ์ด 29 ํ‘œ 11. ์•…๋ ฅ(kg)์˜ ๋ณ€ํ™” 30 ํ‘œ 12. ์œ—๋ชธ ์ผ์œผํ‚ค๊ธฐ(ํšŒ/๋ถ„)์˜ ๋ณ€ํ™” 31 ํ‘œ 13. ์œ—๋ชธ ์•ž์œผ๋กœ ๊ตฝํžˆ๊ธฐ(cm)์˜ ๋ณ€ํ™” 32 ํ‘œ 14. ํ•˜๋ฒ„๋“œ ์Šคํ… ํ…Œ์ŠคํŠธ(PEI)์˜ ๋ณ€ํ™” 33 ํ‘œ 15. ICT ๊ทธ๋ฃน๊ณผ Hospital ๊ทธ๋ฃน์˜ ๋Œ€์‚ฌ์ฆํ›„๊ตฐ ์œ„ํ—˜์š”์ธ์˜ ์ฐจ์ด 34 ํ‘œ 16. ํ—ˆ๋ฆฌ๋‘˜๋ ˆ(cm)์˜ ๋ณ€ํ™” 35 ํ‘œ 17. ์ค‘์„ฑ์ง€๋ฐฉ(mg/dL)์˜ ๋ณ€ํ™” 36 ํ‘œ 18. HDL-C(mg/dL)์˜ ๋ณ€ํ™” 37 ํ‘œ 19. ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••(mmHg)์˜ ๋ณ€ํ™” 38 ํ‘œ 20. ์ด์™„๊ธฐ ํ˜ˆ์••(mmHg)์˜ ๋ณ€ํ™” 39 ํ‘œ 21. ๊ณต๋ณตํ˜ˆ๋‹น(mg/dL)์˜ ๋ณ€ํ™” 40 ๊ทธ ๋ฆผ ๋ชฉ ์ฐจ ๊ทธ๋ฆผ 1. ์—ฐ๊ตฌ์„ค๊ณ„ 16 ๊ทธ๋ฆผ 2. ICT๋ฅผ ํ™œ์šฉํ•œ ์šด๋™์ฒ˜๋ฐฉ ํ”„๋กœ๊ทธ๋žจ 21 ๊ทธ๋ฆผ 3. ์ฒด์ค‘์˜ ๋ณ€ํ™” 23 ๊ทธ๋ฆผ 4. ๊ณจ๊ฒฉ๊ทผ๋Ÿ‰์˜ ๋ณ€ํ™” 24 ๊ทธ๋ฆผ 5. ์ฒด์ง€๋ฐฉ๋Ÿ‰์˜ ๋ณ€ํ™” 25 ๊ทธ๋ฆผ 6. ์ฒด์ง€๋ฐฉ๋ฅ ์˜ ๋ณ€ํ™” 26 ๊ทธ๋ฆผ 7. BMI์˜ ๋ณ€ํ™” 27 ๊ทธ๋ฆผ 8. WHR์˜ ๋ณ€ํ™” 28 ๊ทธ๋ฆผ 9. ์•…๋ ฅ์˜ ๋ณ€ํ™” 30 ๊ทธ๋ฆผ 10. ์œ—๋ชธ ์ผ์œผํ‚ค๊ธฐ์˜ ๋ณ€ํ™” 31 ๊ทธ๋ฆผ 11. ์œ—๋ชธ ์•ž์œผ๋กœ ๊ตฝํžˆ๊ธฐ์˜ ๋ณ€ํ™” 32 ๊ทธ๋ฆผ 12. ํ•˜๋ฒ„๋“œ ์Šคํ… ํ…Œ์ŠคํŠธ์˜ ๋ณ€ํ™” 33 ๊ทธ๋ฆผ 13. ํ—ˆ๋ฆฌ๋‘˜๋ ˆ์˜ ๋ณ€ํ™” 35 ๊ทธ๋ฆผ 14. ์ค‘์„ฑ์ง€๋ฐฉ์˜ ๋ณ€ํ™” 36 ๊ทธ๋ฆผ 15. HDL-C์˜ ๋ณ€ํ™” 37 ๊ทธ๋ฆผ 16. ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์˜ ๋ณ€ํ™” 38 ๊ทธ๋ฆผ 17. ์ด์™„๊ธฐ ํ˜ˆ์••์˜ ๋ณ€ํ™” 39 ๊ทธ๋ฆผ 18. ๊ณต๋ณตํ˜ˆ๋‹น์˜ ๋ณ€ํ™” 40Maste

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    Analysis of relative efficiency of the public health care post and its related factors

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    ๊ฐ„ํ˜ธํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ๋ณธ ์—ฐ๊ตฌ๋Š” ์ผ์ฐจ๋ณด๊ฑด์˜๋ฃŒ์˜ ์ค‘์‹ฌ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋Š” ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ์ƒ๋Œ€์  ํšจ์œจ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ณด๊ฑด์ง„๋ฃŒ์›์˜ ํŠน์„ฑ ๋ฐ ์ œ๋ฐ˜ํ™˜๊ฒฝ ํŠน์„ฑ ์š”์ธ์„ ๋ถ„์„ํ•˜์—ฌ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ํšจ์œจ์„ฑ์„ ํ•ฉ๋ฆฌ์ ์œผ๋กœ ๊ฐœ์„ ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ๋งˆ๋ จ์— ํ•˜๋‚˜์˜ ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜๊ณ ์ž ์‹œ๋„๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์€ ์„ธ ๊ฐœ ๋„์— ์†Œ์žฌํ•œ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๋ฅผ ๊ทผ์ ‘๋ชจ์ง‘๋‹จ์œผ๋กœ ํ•˜์—ฌ ๋ฌด์ž‘์œ„ ํ‘œ์ถœํ•œ 177๊ฐœ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์ด๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์€ ์ด๋“ค ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์— ๊ทผ๋ฌดํ•˜๋Š” 177๋ช…์˜ ๋ณด๊ฑด์ง„๋ฃŒ์›๋“ค์— ๋Œ€ํ•ด ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ํšจ์œจ์„ฑ ์ธก์ •์„ ์œ„ํ•ด ํˆฌ์ž…๋ณ€์ˆ˜๋กœ ๋ณด๊ฑด์‚ฌ์—…์šด์˜๋น„์™€ ์ธ๊ฑด๋น„๋ฅผ ์„ค์ •ํ•˜์˜€์œผ๋ฉฐ ์‚ฐ์ถœ๋ณ€์ˆ˜๋กœ๋Š” ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ๋‚ด์†Œ์ž์ˆ˜, ๊ฐ€์ •๋ฐฉ๋ฌธ๊ฑด์ˆ˜, ์ „ํ™”์ƒ๋‹ด๊ฑด์ˆ˜, ์ง‘๋‹จ๋ณด๊ฑด๊ต์œก๊ฑด์ˆ˜, ์ผ์ฐจ๋ณด๊ฑด์˜๋ฃŒ๊ธฐ๋Šฅ ์ดํ–‰์ •๋„, ํŠน์ˆ˜์‚ฌ์—…๊ฑด์ˆ˜๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ํ•œํŽธ, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ํšจ์œจ์„ฑ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์š”์ธ์„ ๊ทœ๋ช…ํ•˜๊ณ ์ž ๋ณด๊ฑด์ง„๋ฃŒ์› ๊ด€๋ จ ํŠน์„ฑ, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ํ™˜๊ฒฝ์  ํŠน์„ฑ, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ์‚ฌ์—… ์ง€์› ์ •๋„ ๋“ฑ์˜ ์š”์ธ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์€ 2003๋…„ 9์›” 20์ผ์—์„œ 11์›” 5์ผ๊นŒ์ง€ ์šฐํŽธ์„ ํ†ตํ•œ ์„ค๋ฌธ์กฐ์‚ฌ๋กœ ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ํšจ์œจ์„ฑ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ์ž๋ฃŒํฌ๋ฝ๋ถ„์„์ด ์‚ฌ์šฉ๋˜์—ˆ๊ณ  ํšจ์œจ์„ฑ์— ์˜ํ–ฅ์š”์ธ์„ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด t ํ…Œ์ŠคํŠธ(๋˜๋Š” ฯ‡2 ํ…Œ์ŠคํŠธ) ๋ฐ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1. ์กฐ์‚ฌ๋Œ€์ƒ 177๊ฐœ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ๊ด€๋ฆฌ์šด์˜๋น„ ๋ฐ ๋ณด๊ฑด์‚ฌ์—…๋น„๋Š” ํ‰๊ท  2,010๋งŒ์›์œผ๋กœ ์ตœ์†Œ 303๋งŒ์›์—์„œ ์ตœ๋Œ€ 7,099๋งŒ์›๊นŒ์ง€ ํˆฌ์ž…ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ํ•œํŽธ, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ์ธ๊ฑด๋น„๋Š” ํ‰๊ท  3,361๋งŒ์›์ด์—ˆ์œผ๋ฉฐ ์ตœ์†Œ 2,000๋งŒ์› ์ตœ๋Œ€ 5,117๋งŒ์›์ด์—ˆ๋‹ค. 2. ๋Œ€์ƒ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ์›”ํ‰๊ท  ์ˆ˜ํ–‰๊ฑด์ˆ˜๋Š” ๋‚ด์†Œ์ž์ˆ˜ 280๋ช…, ๊ฐ€์ •๋ฐฉ๋ฌธ๊ฑด์ˆ˜ 28.8๊ฑด, ์ „ํ™”์ƒ๋‹ด๊ฑด์ˆ˜ 28.4๊ฑด, ์ง‘๋‹จ๋ณด๊ฑด๊ต์œก๊ฑด์ˆ˜ 154๊ฑด์ด์—ˆ์œผ๋ฉฐ, ์ผ์ฐจ๋ณด๊ฑด์˜๋ฃŒ๊ธฐ๋Šฅ ์ดํ–‰์ •๋„๋Š” ํ‰๊ท  73.8%์ด์—ˆ๋‹ค. 3. ํšจ์œจ์„ฑ ์ธก์ •๊ฒฐ๊ณผ ์กฐ์‚ฌ๋Œ€์ƒ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ 177๊ฐœ์˜ ํšจ์œจ์„ฑ์€ ๊ทœ๋ชจ์ˆ˜์ต๊ณ ์ •์„ ์ „์ œ๋กœ ํ–ˆ์„ ๋•Œ ๋ชจํ˜•1์—์„œ ํ‰๊ท  0.75, ๋ชจํ˜•2์—์„œ ํ‰๊ท  0.62์˜€์œผ๋ฉฐ, ํšจ์œจ์น˜ 1์— ๋„๋‹ฌํ•œ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๋Š” ๋ชจํ˜•1์—์„œ 26๊ฐœ(14.7%), ๋ชจํ˜•2์—์„œ 17๊ฐœ(9.6%)์˜€๋‹ค. ๊ทœ๋ชจ์ˆ˜์ต๊ฐ€๋ณ€์„ ์ „์ œ๋กœ ํ•˜์˜€์„ ๊ฒฝ์šฐ ํšจ์œจ์น˜ 1์— ๋„๋‹ฌํ•œ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๋Š” ๋ชจํ˜•1์—์„œ 42๊ฐœ(23.7%), ๋ชจํ˜•2์—์„œ 27๊ฐœ (15.3%)์˜€์œผ๋ฉฐ ํ‰๊ท ์€ ๊ฐ๊ฐ 0.82์™€ 0.78์ด์—ˆ๋‹ค. 4. ๋‹จ์ˆœ๋ถ„์„์—์„œ ๋ณด๊ฑด์ง„๋ฃŒ์›์˜ ์—ฐ๋ น๊ณผ ์ง๋ฌด์ˆ˜ํ–‰๋Šฅ๋ ฅ, ๋งŒ์„ฑ์งˆํ™˜์ž ๋น„์œจ, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๋ฅผ 1์ˆœ์œ„๋กœ ์ด์šฉํ•˜๋Š” ๋น„์œจ ๋“ฑ์ด ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ํšจ์œจ์„ฑ๊ณผ ์œ ์˜ํ•˜๊ฒŒ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ง€์›์ •๋„, ์ง€๋ฐฉ์˜ํšŒ์˜ ์ง€์›์ •๋„, ๋ณด๊ฑด์†Œ์™€์˜ ํ˜‘๋ ฅ์ •๋„, ์šด์˜ํ˜‘์˜ํšŒ ํ˜‘๋ ฅ์ •๋„๊ฐ€ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ํšจ์œจ์„ฑ๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 5. ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ชจํ˜•1์—์„œ ๋ณด๋ฉด, ๋ณด๊ฑด์ง„๋ฃŒ์›์˜ ์—…๋ฌด๊ด€๋ จ ๋ณด์œ  ์ž๊ฒฉ์ฆ ์ˆ˜๊ฐ€ ๋งŽ์„์ˆ˜๋ก, ๊ทธ๋ฆฌ๊ณ  ๋ณด๊ฑด์ง„๋ฃŒ์›์˜ ์ง๋ฌด์ˆ˜ํ–‰๋Šฅ๋ ฅ์ด ๋†’์„์ˆ˜๋ก ํšจ์œจ์ ์ด์—ˆ๋‹ค. ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ํ™˜๊ฒฝ์  ํŠน์„ฑ์—์„œ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ์†Œ์žฌ ์ง€์—ญ์ด C๋„ ์ผ ๊ฒฝ์šฐ A์ง€์—ญ์ธ ๊ฒฝ์šฐ๋ณด๋‹ค ํšจ์œจ์ ์ด์—ˆ์œผ๋ฉฐ, ๋งŒ์„ฑ์งˆํ™˜์ž ๋น„์œจ์ด ๋‚ฎ์€ ์ง€์—ญ์ผ์ˆ˜๋ก, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๋ฅผ 1์ˆœ์œ„๋กœ ์ด์šฉํ•˜๋Š” ๋น„์œจ์ด ๋†’์€ ๊ณณ์ผ์ˆ˜๋ก ํšจ์œจ์ ์ด์—ˆ๋‹ค. ๋˜ํ•œ, ์ง€๋ฐฉ์˜ํšŒ์˜ ํ˜‘์กฐ์ •๋„๊ฐ€ ํด์ˆ˜๋ก, ์šด์˜ํ˜‘์˜ํšŒ ํ˜‘๋ ฅ์ •๋„๊ฐ€ ํด์ˆ˜๋ก ํšจ์œจ์ ์ด์—ˆ๋‹ค. ๋ชจํ˜•2์—์„œ๋Š”, ๋ณด๊ฑด์ง„๋ฃŒ์›์ด ํ•™์‚ฌํ•™์œ„๋ฅผ ์†Œ์ง€ํ•˜์˜€์„ ๊ฒฝ์šฐ, ์—…๋ฌด๊ด€๋ จ๋ณด์œ  ์ž๊ฒฉ์ฆ ์ˆ˜๊ฐ€ ๋งŽ์„์ˆ˜๋ก, ๋ณด๊ฑด์ง„๋ฃŒ์†Œ ์†Œ์žฌ ์ง€์—ญ์ด C๋„ ์ผ ๊ฒฝ์šฐ A์ง€์—ญ์ธ ๊ฒฝ์šฐ๋ณด๋‹ค ํšจ์œจ์ ์ด์—ˆ์œผ๋ฉฐ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๋ฅผ 1์ˆœ์œ„๋กœ ์ด์šฉํ•˜๋Š” ๋น„์œจ์ด ๋†’์€ ๊ณณ์ผ์ˆ˜๋ก ํšจ์œจ์ ์ด์—ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ๊ฐ€ ์ผ์ฐจ๋ณด๊ฑด์˜๋ฃŒ๊ธฐ๊ด€์œผ๋กœ์„œ ํšจ์œจ์ ์œผ๋กœ ์šด์˜๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ณด๊ฑด์ง„๋ฃŒ์›์˜ ์ „๋ฌธ์  ๋Šฅ๋ ฅ์„ ์ฆ์ง„์‹œํ‚ค๊ณ  ์ง€์—ญ์‚ฌํšŒ์˜ ํ˜‘์กฐ๋ฅผ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋„๋ก ์ •์ฑ…์  ์ง€์›์„ ํฌํ•จํ•œ ์ œ๋ฐ˜ ๋ฐฉ์•ˆ์ด ์ ๊ทน์ ์œผ๋กœ ๊ฐ•๊ตฌ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ, ๋งŒ์„ฑ์งˆํ™˜๊ด€๋ฆฌ ์œ„์ฃผ์˜ ๊ธฐ๋Šฅ์„ ๊ฐ•ํ™”ํ•จ์œผ๋กœ์จ ๋ณด๊ฑด์ง„๋ฃŒ์†Œ์˜ ํšจ์œจ์„ฑ์„ ๋†’์ผ ์ˆ˜ ๊ฒƒ์ด๋‹ค. [์˜๋ฌธ]The purpose of this study was to evaluate the relative efficiency of Public Health Care Posts, and to offer evidence that would go to improving the Posts'' feasibility by analyzing the characteristics and general conditions of Public Health Care Posts. The research sample was determined through a random selection of 177 Public Health Care Posts in three provinces. Research data was collected through questionnaire distributed to 177 Community health practitioners(CHPs) at the Public Health Care Posts. In order to measure relative efficiency, input variables such as operational costs and salary expenses of the various Posts were fixed, while the dependent revenue output variables were the number of visitors, home visits, consultation by telephone, health education, and special program, and the level of compliance of primary health care. To identify the factors influencing the efficiency of Public Health Care Posts, analysis was undertaken on characteristics of the CHP, community characteristics, and the level of support and participation for the Post. Data from the questionnaire was gathered through mail from September 20 to November 5, 2003, and to measure the Posts'' efficiency levels, the data envelopment analysis model was used while t-test (also ฯ‡2-test) and logistic regression analysis was used to identify the factors influencing the efficiency of the Posts. Research findings were as follows: 1. The mean average of administrative and operations cost of Public Health Care Posts'' was 20.10 million won, ranging from 3.03 million won and 70.99 million won at either end. Also, the mean salary was 33.61 million won, ranging from as low as 20.0 million to as high as 51.17 million won per year. 2. The number of visitors at the the Posts were 280 cases/month, home visits 28.8cases/month, consultation by telephone 28.4 cases/month, and health education 154 cases/month. In turn, the compliance of primary health care made up 73.8% of all cases. 3. The relative efficiency of the 177 Public Health Care Posts, when assuming constant return to scale, was 0.75 in Module 1 and 0.62 in Module 2 was 0.62, and there were 25(14.1%) Public Health Care Posts in Module 1 and 17(9.6%) in Module 2. And the efficiency, when assuming variable return to scale, were 42(23.7%) of Posts in Module 1 and 27(15.3%) in Module 2, with an average of 0.82 and 0.78, respectively. 4. While conducting t-test (also ฯ‡2-test) to find whether a relationship exists between the Public Health Care Posts'' efficiency with its related factors, it was found that there is a statistical relationship the age of CHP''s and their job competency, the ratio of patients in chronic conditions, and whether or not visitors used the Posts as their primary source of health care provider. It was also found that a relationship exists between a Public Health Care Post''s efficiency with the level of support from local self-government and community health center, the level of community participation. 5. Logistic regression analysis revealed that in Module 1, Public Health Care Posts the efficiency of were higher depending on the number of professional licenses held by CHP, and the CHP''s job competency. Insofar as the location of Public Health Care Posts was concerned, Public Health Care Posts in province C were found to be more efficient than in province A, and the efficiency was higher where the ratio of patients in chronic condition was lower, and where the ratio of visitors using the Post as their primary source of health care provider was higher. The efficiency is higher when the level of support from local assembly and of participation of the Steering Council is higher. In Module 2, the level of the Posts'' efficiency were higher where CHPs were possessing the bachelor''s degree, and more professional licenses were held. And as far as the location was concerned, the efficiency of Public Health Care Posts in province C were higher than those in province A, where the Posts'' efficiency was higher when visitors used the Posts as their primary source of health care provider. In conclusion, in order for Public Health Care Posts to operate efficiently as primary health care organization, a general support plan should be considered to raise the specialization of CHP and expand the scope of cooperation from their community. Also, the efficiency of Health Care Posts can be enhanced by improving the care for patients in chronic condition.ope
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