100 research outputs found

    Capability-based Evaluation Framework for Basic Education Quality

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธ€๋กœ๋ฒŒ๊ต์œกํ˜‘๋ ฅ์ „๊ณต, 2022.2. ์œ ์„ฑ์ƒ.๋Œ€ํ‘œ์ ์œผ๋กœ MDG2, EFA, SDG4์™€ ๊ฐ™์€ ๊ตญ์ œ์‚ฌํšŒ ๊ต์œก ๊ฐœ๋ฐœ ํ˜‘๋ ฅ์˜ ๋ชฉํ‘œ๋Š” ์ „์„ธ๊ณ„ ์ˆ˜๋งŽ์€ ์•„์ด๋“ค์—๊ฒŒ ๊ต์œก์˜ ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•˜์˜€๋‹ค. ํŠน๋ณ„ํžˆ MDG ๋‘๋ฒˆ์งธ ๋ชฉํ‘œ์ธ โ€˜Achieving universal primary education(๋ณดํŽธ์  ์ดˆ๋“ฑ ๊ต์œก์˜ ๋‹ฌ์„ฑ)โ€™์„ ํ†ตํ•ด ๋งŽ์€ ์ˆ˜์˜ ์•„์ด๋“ค์„ ์ดˆ๋“ฑํ•™๊ต์— ๋“ฑ๋กํ•˜๊ฒŒ ํ•˜๋Š” ํš๊ธฐ์ ์ธ ์–‘์  ์„ฑ๊ณผ๋ฅผ ์ด๋ค˜๋‹ค. ์ดํ›„ ๊ตญ์ œ ์‚ฌํšŒ๋Š” ์ด๋Ÿฌํ•œ ์„ฑ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ณด๋‹ค ๋„์ „์ ์ธ ๋ชฉํ‘œ๋กœ์„œ(SDG4) โ€˜๋ชจ๋‘์—๊ฒŒ ํ†ตํ•ฉ์ ์ด๊ณ  ํ‰๋“ฑํ•œ ์–‘์งˆ์˜ ํ‰์ƒํ•™์Šต ๊ธฐํšŒโ€™ ํ™•๋ณด๋ฅผ ์„ธ์› ๋‹ค(UN 2015). ์‹ค์ œ ๊ต์œก ๋ถ„์•ผ๋กœ ํˆฌ์ž…๋œ ์›์กฐ์˜ ์–‘์€ ์ง€๋‚œ 20๋…„๊ฐ„ ๊พธ์ค€ํžˆ ์ฆ๊ฐ€ํ•˜์—ฌ 2019๋…„์—๋Š” ์•ฝ 159์–ต ๋‹ฌ๋Ÿฌ์— ๋‹ฌํ•˜์˜€๋‹ค(OECD CRS 2021). ํ•˜์ง€๋งŒ ๊ตญ์ œ์‚ฌํšŒ์˜ ๊พธ์ค€ํ•œ ์ •์ฑ…์  ์žฌ์ •์  ์ง€์›์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋งŽ์€ ํ†ต๊ณ„์ž๋ฃŒ๋Š” MDG์ดํ›„ ๊ตญ์ œ ์‚ฌํšŒ ๊ต์œก ์ง€ํ‘œ์˜ ๋ณ€ํ™”๊ฐ€ ๋ฉˆ์ถ”๊ฑฐ๋‚˜ ์‹ฌ์ง€์–ด ์ผ๋ถ€ ์ง€์—ญ์˜ ๊ฒฝ์šฐ ์•…ํ™”๋˜๋Š” ๋ชจ์Šต์„ ์ „๋งํ•˜๊ณ  ์žˆ๋‹ค(UNESCO 2019; UN 2015). ํŠนํžˆ MDG ๋ชฉํ‘œ ๋‹ฌ์„ฑ์˜ ์ฃผ์š” ์ฒ™๋„์˜€๋˜ ๋“ฑ๋ก๋ฅ  ์ง€ํ‘œ๋ฅผ ์ œ์™ธํ•˜๊ณ ๋Š”, ์ค‘๋„ํƒˆ๋ฝ๋ฅ , ์กธ์—…๋ฅ  ๋“ฑ์˜ ์ง€ํ‘œ๋Š” ์ „ํ˜€ ๊ฐœ์„ ์˜ ์—ฌ์ง€๋ฅผ ๋ณด์ด์ง€ ์•Š๊ณ  ์žˆ๋‹ค. ์ง€ํ‘œ(Indicator)๋ฅผ ์–ด๋– ํ•œ ์ƒํƒœ์˜ ๋ณธ์งˆ์ด๋‚˜ ๊ณผ์ •์˜ ๊ฐ€์žฅ ์ ์ ˆํ•œ ์ˆœ๊ฐ„์„ ํฌ์ฐฉํ•˜๋Š” ๋„๊ตฌ ์ค‘ ํ•˜๋‚˜๋ผ๊ณ  ๊ฐ€์ •ํ•˜์˜€์„ ๋•Œ, ๋“ฑ๋ก๋ฅ  ์ง€ํ‘œ๋Š” ๊ต์œก ๋ฐœ์ „์˜ ๊ธด ๊ณผ์ •์˜ ๋ฌธ์„ ์—ฌ๋Š” ๊ธฐ์ดˆ ๋‹จ๊ณ„๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ต์œก ๋ฐœ์ „์„ ๋…ผ์˜ํ•  ๋•Œ ๊ฐ€์žฅ ๋ณดํŽธ์ ์ด๊ณ  ๊ธฐ์ดˆ์ ์ธ ์ง€ํ‘œ๋กœ ํ™œ์šฉ๋˜๋Š” ์ด์œ ์ด๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋“ฑ๋ก๋ฅ  ์ง€ํ‘œ๋Š” UN์ด Tier I ์œผ๋กœ ๊ตฌ๋ถ„ํ•œ ๊ฒƒ๊ณผ ๊ฐ™์ด ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์˜ ์šฉ์ด์„ฑ ์ธก๋ฉด์—์„œ๋„ ๊ธฐ์ดˆ์ ์ด๋ฉฐ, ์ง€ํ‘œ ์ธก์ •์˜ ๋ฐฉ๋ฒ•๋ก ์  ์ธก๋ฉด์—์„œ๋„ ๊ธฐ์ดˆ์ ์ด๋‹ค. ๊ทธ๋ ‡๋‹ค๋ฉด ์ค‘๋„ํƒˆ๋ฝ๋ฅ , ์กธ์—…๋ฅ , ํ˜น์€ ํ•™์—…์„ฑ์ทจ์œจ๊ณผ ๊ฐ™์€ ์ง€ํ‘œ๋Š” ์–ด๋– ํ•œ๊ฐ€? ์ €์ž๊ฐ€ ๊ณผ๋„ํ•œ ์ผ๋ฐ˜ํ™”์˜ ์œ„ํ—˜์„ ๊ฐ์ˆ˜ํ•˜๊ณ ๋„ ์ƒ์‹์  ์ˆ˜์ค€์—์„œ ๋งํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์€, ํ•ด๋‹น ์ง€ํ‘œ๋“ค์€ ๋“ฑ๋ก๋ฅ  ์ง€ํ‘œ์™€๋Š” ๋‹ค๋ฅธ ์ˆ˜์ค€์˜ ๋ณต์žก์„ฑ์„ ๋‚ดํฌํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋“ฑ๋ก๋ฅ  ์™ธ์˜ ๊ต์œก ์ง€ํ‘œ์˜ ๋ณ€ํ™”๊ฐ€ ๋ฉˆ์ถ˜ ์ด์œ ๋ฅผ ๋‹จ์ˆœํžˆ ์ง€ํ‘œ์˜ ํ•ด์„์  ๋‹ค์–‘์„ฑ ํ˜น์€ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์˜ ์–ด๋ ค์›€์—์„œ ์ฐพ๊ณ ์ž ํ•˜์ง€ ์•Š๋Š”๋‹ค. ์˜คํžˆ๋ ค ์šฐ๋ฆฌ๊ฐ€ ํŠน์ • ๋ชฉํ‘œ์˜ ๋‹ฌ์„ฑ์„ ์ ๊ฒ€ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰ํ•˜๋Š” ํ‰๊ฐ€์—์„œ ์ง€ํ‘œ๋ฅผ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์ด ์–ผ๋งˆ๋‚˜ ๋‹จํŽธ์ ์ด๋ฉฐ ๊ฒฐ๊ณผ ์ค‘์‹ฌ์ ์ธ ํ•ด์„์„ ๊ฐ€์ ธ์˜ค๋Š”์ง€ ๊ทธ ํ•œ๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ด๊ณ ์ž ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ๊ฒƒ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ๋Œ€์•ˆ์œผ๋กœ์„œ ๊ฐœ๋ฐœํ‰๊ฐ€์—์„œ ๊ต์œก์˜ ์งˆ์„ ํ‰๊ฐ€ํ•  ๋•Œ ์ง€๊ธˆ๊นŒ์ง€ ํ™œ๋ฐœํ•˜๊ฒŒ ๋…ผ์˜๋˜์ง€ ์•Š์•˜๋˜ โ€˜ํ•™์ƒ๊ณผ ๊ต์ˆ˜์ž์™€์˜ ํ•™์Šต ๊ณต๊ฐ„์—์„œ์˜ ์ƒํ˜ธ ์ž‘์šฉโ€™์„ ํ‰๊ฐ€์˜ ํ•œ ๊ฐ€์šด๋ฐ๋กœ ๋†“๋Š” ํ‰๊ฐ€ํ‹€์„ ์ œ์•ˆํ•œ๋‹ค. ํ›„์ƒ ๊ฒฝ์ œ ์ด๋ก ์œผ๋กœ ๋…ธ๋ฒจ ๊ฒฝ์ œํ•™์ƒ์„ ๋ฐ›์€ ์ธ๋„์˜ ๊ฒฝ์ œ ์ฒ ํ•™์ž ์•„๋งˆํ‹ฐ์•„์„ผ์€ 1990๋…„๋Œ€ Capability Approach๋ฅผ ํ†ตํ•ด ๊ฐœ๋ฐœํ˜‘๋ ฅ์˜ ์ˆ˜๋งŽ์€ ๋ถ„์•ผ์— ๊ฐ€ํžˆ ํ˜๋ช…์  ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ํŠนํžˆ ์‚ถ์˜ ์งˆ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ฐœ๋…์  ํ‹€์„ ์ œ๊ณตํ•˜๋Š”๋ฐ ํฐ ๊ธฐ์—ฌ๋ฅผ ํ–ˆ๋Š”๋ฐ, ๊ทธ๊ฐ„ GDP๋กœ๋งŒ ํ‰๊ฐ€๋˜์—ˆ๋˜ ๊ตญ๊ฐ€์˜ ๋ฐœ์ „ ์ •๋„๋ฅผ ๊ต์œก๊ณผ ๋ณด๊ฑด์˜ ์˜์—ญ๊นŒ์ง€ ํ™•์žฅ ์‹œ์ผฐ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์ด ์ œ์‹œํ•˜๋Š” ๊ต์œก์˜ ์งˆ ํ‰๊ฐ€ํ‹€์„ ๊ฐœ๋…ํ™”ํ•˜๋Š”๋ฐ ์žˆ์–ด Capability Approach๊ฐ€ ์ด๋ก ์  ๊ทผ๊ฑฐ๋กœ์„œ ๊ฐ–๋Š” ํ•ต์‹ฌ์  ๊ธฐ์—ฌ๋Š” ๊ฐœ์ธ๊ณผ ์‚ฌํšŒ์˜ โ€˜๋‹ค์–‘์„ฑโ€™์„ ํ‰๊ฐ€์˜ ๋งค์šฐ ์ค‘์š”ํ•œ ์š”์†Œ๋กœ ํฌํ•จ์‹œํ‚จ๋‹ค๋Š” ๊ฒƒ์— ์žˆ๋‹ค. ์ฆ‰, ๊ฐœ์ธ์˜ ์„ ํƒ์˜ ๊ณผ์ •๊ณผ ๊ทธ ๊ฒฐ๊ณผ๋Š” ๊ฐœ์ธ์ , ์‚ฌํšŒ์ , ํ™˜๊ฒฝ์  ์š”์ธ์˜ ๋‹ค์–‘ํ•œ ์กฐํ•ฉ์— ์˜ํ•œ ์˜ํ–ฅ์˜ ๊ฒฐ๊ณผ์ž„์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค. ์ด๊ฒƒ์€ ์„ผ์— ์˜ํ•ด conversion factor๋ผ๋Š” ์šฉ์–ด๋กœ ํ‘œํ˜„๋˜์—ˆ๋‹ค. ๊ต์œก์˜ ์งˆ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ์งˆ์  ์ ‘๊ทผ์€ ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์€ ๋…ธ๋ ฅ๊ณผ ์žฌ์›์ด ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ด๋Ÿฌํ•œ ๋Œ€์•ˆ์  ํ‰๊ฐ€ํ‹€์„ ์ œ์•ˆํ•˜๋Š” ๊ฒƒ์€ ๋‹ค์Œ์˜ ์ด์œ ๊ฐ€ ์žˆ๋‹ค. ์ฒซ์งธ, ์ด๋ฏธ ๋งŽ์€ ๊ต์œกํ•™์ž๋“ค์˜ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ต์œก์˜ โ€˜๊ณผ์ •โ€™์˜ ์งˆ์  ๊ฐœ์„ ๊ณผ ๊ต์œก ๋ฐœ์ „๊ณผ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ์คฌ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์—ฌ๊ธฐ์„œ ๋งํ•˜๋Š” ๊ณผ์ •์€ ํ•™์Šต์ž์™€ ๊ต์ˆ˜์ž ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์ด์•ผ๊ธฐํ•˜๋ฉฐ ๊ณต๊ต์œก์˜ ํ˜•ํƒœ์—์„œ๋Š” ๊ต์‹ค ์•ˆ ์ˆ˜์—… ์‹œ๊ฐ„์˜ ๋ชจ์Šต์„ ์˜ˆ๋กœ ๋“ค ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ํ˜„์žฌ์˜ ๊ฐœ๋ฐœ ํ‰๊ฐ€์˜ ์ ‘๊ทผ์€ ์ด๋Ÿฌํ•œ ํ•™์Šต ๊ณผ์ •์— ๋Œ€ํ•œ ํ‰๊ฐ€๋ฅผ ์ฒ ์ €ํ•˜๊ฒŒ ๋ฐฐ์ œํ•˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํŠนํžˆ ๊ฐœ๋ฐœํ˜‘๋ ฅ์˜ ํ”„๋กœ์ ํŠธ์™€ ํ”„๋กœ๊ทธ๋žจ ํ‰๊ฐ€์—์„œ ํ™œ์šฉ๋˜๋Š” ์ง€ํ‘œ๋“ค์€ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์˜ ์šฉ์ด์„ฑ, ์ธก์ • ๊ฐ€๋Šฅ์„ฑ ๋“ฑ์˜ ๊ฒฝ์ œ์  ์ด์œ ๋กœ ๊ฐ€์žฅ ํ•ต์‹ฌ์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋Š” ํ•™์Šต ๊ณผ์ •, ์งˆ์  ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋ฐ˜์˜์ด ๋„ˆ๋ฌด๋‚˜ ๋ฏธํกํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ๊ธฐ์ดˆ๊ต์œก์˜ ์งˆ ํ‰๊ฐ€ํ‹€์€ ์•„๋งˆํ‹ฐ์•„ ์„ผ์˜ Capability Approach์˜ ํ•ต์‹ฌ์  ๊ฐ€์น˜๋ฅผ ๊ธฐ๋ฐ˜ํ•˜์˜€์œผ๋ฉฐ, ๊ธฐ์กด ๊ต์œก ๋ถ„์•ผ์—์„œ ์งˆ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด ํ™œ์šฉ๋˜์–ด์˜จ ์ด๋ก ๊ณผ ์ ‘๊ทผ ๋ฐฉ๋ฒ•์„ ์ƒ๋‹น ๋ถ€๋ถ„ ์ฐจ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ โ€˜๊ธฐ์ดˆ ๊ต์œก์˜ ์งˆ ์ง€ํ‘œ(Index)โ€™๋ฅผ ์ œ์•ˆํ•จ์œผ๋กœ์จ ๋ณด๋‹ค ํฌ๊ด„์ ์ธ ์ ‘๊ทผ์—์„œ์˜ ๊ต์œก์˜ ์งˆ ํ‰๊ฐ€๊ฐ€ ์ด๋ค„์งˆ ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. Capability์— ๊ธฐ๋ฐ˜ํ•œ ํ‰๊ฐ€๋Š” โ€˜์–ด๋–ป๊ฒŒโ€™ ํ‰๊ฐ€ํ•˜๋Š๋ƒ์— ๋Œ€ํ•œ ๋ฐฉ๋ฒ•๋ก ์  ์งˆ๋ฌธ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ โ€˜๋ฌด์—‡์„โ€™ํ†ตํ•ด ๊ต์œก์˜ ์งˆ์„ ํ‰๊ฐ€ํ•˜๋Š๋ƒ์— ๋Œ€ํ•œ ๋ณด๋‹ค ๊ทผ๋ณธ์ ์ธ ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋‹ต์„ ์ฐพ๊ฒŒ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ต์œก๊ฐœ๋ฐœํ˜‘๋ ฅ์—์„œ์˜ ๊ธฐ์ดˆ ๊ต์œก์˜ ์งˆ์„ ์ •์˜ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ๋ณด๋‹ค ๊ต์œก์  ๊ด€์ ์„ ์ œ๊ณตํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.International education development goals such as MDG2, EFA, and SDG4 have helped to provide educational opportunities to thousands of children around the world. Andover the last 20 years, the amount of international aid invested in education sector has steadily increased, reaching around $15.9 billion in 2019. (OECD CRS 2021). A significant quantitative result was achieved in enrolling a large number of children in elementary school through the MDG's second goal, 'Achieving universal primary education.' Since then, the international community has set a more challenging goal (SDG4) to ensure โ€˜inclusive and equitable quality education and promote lifelong learning opportunities for allโ€™ based on these achievements (UN 2015). However, despite steady policy and financial support from the international community, many statistical data predict that changes in international social education indicators after the MDG will stop or even worsen in some regions (UNESCO 2019; UN 2015). In particular, with the exception of the enrollment rate indicator, which was a major measure of achievement of the MDG goal, indicators such as dropout rate and graduation rate show no room for improvement at all. Assuming that the indicator is one of the tools for capturing the essence of any state or the most appropriate moment of the certain process, the enrollment rate indicator can be considered as a first step in a long educational development process. This is why it is used as the most common and basic indicator when discussing educational development. In addition, it is also classified as Tire I indicator by the IAEG-SDGs based on its methodological development and the availability of data. So, what about other indicators such as dropout rates, graduation rates, or academic achievement rates? What I can say at a common-sense level, even at the risk of overgeneralization, is that these indicators contain a higher level of complexity than the enrollment rate indicators. This study is not intended to blame those indicators for the slowing of educational development. Rather, it tries to highlight the shortcomings of how fragmentary and result-oriented the interpretation of the indicators of development evaluation to ensure the achievement of specific goals. And, as an alternative to overcoming it, I propose an evaluation framework that places the โ€˜interaction between students and instructors in the learning spaceโ€™, which has not been actively discussed so far in development evaluation, in the center of evaluation when evaluating the quality of education. Indian economic philosopher Amartya Sen, who won the Nobel Prize in Economics for his theory of welfare economy, had a truly revolutionary effect on numerous fields of development cooperation through his Capability Approach in the 1990s. In particular, he made a great contribution to providing a conceptual framework for measuring the well-being, as he extended the development of the country, which had been evaluated only by GDP, to the fields of education and health. The key contribution of the Capability approach as a theoretical framework in conceptualizing the evaluation framework for quality of education presented in this study is that it includes the 'diversity' of individuals, social and environmental contexts as a very important factor in the evaluation. In other words, it reveals that the process and results of individual choices are influenced by a various combination of personal, social, and environmental factors. Sen referred to this as the conversion factor. A qualitative approach to assessing the quality of education requires a relatively significant amount of time and resources. Nevertheless, the proposal of such an alternative evaluation framework is for the following reasons. First, it is because many educators have already shown a correlation between the qualitative improvement of the education and โ€˜processโ€™ of teaching and learning. The process here refers to the interaction between the learner and the instructor. Second, it is because the current development evaluation approach completely excludes evaluation of this learning process. In particular, the indicators used in the evaluation of development cooperation projects and programs do not reflect the most important learning process and qualitative change for economic reasons such as ease of data collection and measurability. The evaluation framework of basic education quality proposed in this dissertation is based on the core value of Amartya Sen's capability approach, and it borrows many theories and approaches that have been used for quality evaluation in the education field. In addition, to assist more comprehensive manner of evaluation of quality education, the โ€˜Basic Education Quality Indexโ€™ is proposed. It is expected the Capability-based evaluation will contribute to find answers to the more fundamental question of evaluating the quality of education through โ€˜whatโ€™ as well as the methodological question of โ€˜howโ€™ to evaluate. Finally, I wish it is also to bring a more educational perspective in defining and evaluating the quality of basic education in educational development cooperation.I. Introduction 1 1.1 Research Background and Objectives 1 1.2 Capability Approach as a Theoretical Methodology 7 1.3 Research Scope and Structure of Dissertation 11 II. Literature Review 14 2.1 Core Concepts of the Capability Approach 14 2.2 Critiques and the Capability List 21 2.3 Capability and Education 25 III. Quality Issues in Education Development Cooperation 37 3.1 Agenda Shifting from Accessibility to Quality 37 3.2 Assessing the Quality of Education 45 IV. Results-based Evaluation in Development Cooperation 57 4.1 Development Evaluation and Quality Measurement 58 4.2 Results-based Approach in Development Evaluation 69 4.3 Challenges of Results-based Approach 81 V. Capability-based Evaluation Framework for Quality Education 90 5.1 Evaluation Framework for Basic Education Quality 94 5.2 Discussions 105 5.3 Application and Methodology 138 VI. Conclusion 148 6.1 Evaluation of Education Quality and Capability 148 6.2 Limitation of the Study 153 6.3 Final Remarks 155๋ฐ•

    Sex differences in risk factors for depressive symptoms in patients with COPD: The 2014 and 2016 Korea National Health and Nutrition Examination Survey

    Get PDF
    Background: Although depression is a common comorbidity of chronic obstructive pulmonary disease (COPD), the role of sex remains unexplored. We evaluated sex differences of risk factors of depressive symptoms in adults with COPD. Methods: This was a population-based cross-sectional study using data from the 2014 and 2016 Korea National Health and Nutrition Examination Survey. Spirometry was used to identify patients with COPD, defined as a FEV1/FVC ratio < 0.7. Presence of depressive symptoms was defined as a total score โ‰ฅ 5 on the Patient Health Questionnaire-9. Results: 17.8% of participants expressed depressive symptoms. Relative regression analysis revealed that female sex (RR 2.38; 95% CI 1.55-3.66; p < 0.001), living alone (RR 1.46; 95% CI 1.08-1.97; p = 0.013), current smoker (RR 1.70; 95% CI 1.15-2.52; p = 0.008), underweight (RR 1.58 95% CI 1.00-2.49; p = 0.049), and GOLD Stage III/IV (RR 1.92; 95% CI 1.19-3.09; p = 0.007) were the risk factors for depressive symptoms. Low income, living alone, multiple chronic disorders, and low BMI were risk factors of depressive symptoms in male, whereas low educational attainment, urban living, and current smoking were risk factors in female. Conclusions: Female sex is a main risk factor of depressive symptoms in adults with COPD. As risk factors of depressive symptoms in COPD patients vary according to their sex, different approaches are needed to manage depression in males and females with COPD.ope

    A Study on the Fuel Consumption & Green Gas Emission of Coastal Ships

    Get PDF
    One of the key tasks for human being to take seems to be the reduction of fossil fuel consumption in order to save our planet from the disaster caused be climate changes. World wide effort to reduce fossil fuel consumption seems to be enforced. Accordingly, Korean government implemented the basic law for low carbon green growth in January 2010, and detailed guideline followed in March 2011. In the field of shipping industry the government launched a second stage pilot project of vessel green gas reduction in which 2 companies participated. In 2015 EEDI was applied to the newly building vessels to achieve the target of 37% green gas reduction up to 2030. This study, therefore, tried to analyse the characteristics of fuel consumption and green gas emission of coastal vessels based on the empirical data gathered from a domestic company. For this vessel operating data containing sailing distance, port time, sailing time, speed, cargo loading ratio etc. were gathered for 12 vessels for the period of 5 years. The analysis revealed some meaningful results and based on them some implication for the reduction of vessel green gas emission.๋ชฉ ์ฐจ List of Tables ii List of Figures iii Abstract iv ์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 1.2 ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 4 ์ œ 2 ์žฅ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  6 2.1 ์—ฐ์•ˆ์„ ๋ฐ• ์—ฐ๋ฃŒ์†Œ๋น„์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 6 2.2 ์„ ๋ฐ• ์˜จ์‹ค๊ฐ€์Šค ๊ฐ์ถ•์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 8 ์ œ 3 ์žฅ ๊ตญ๋‚ดยท์™ธ ์˜จ์‹ค๊ฐ€์Šค ์ œ๋„ ๋ฐ ๊ตญ๋‚ด ์—ฐ์•ˆํ•ด์šด ํ˜„ํ™ฉ ๋ถ„์„ 11 3.1 ๊ตญ๋‚ดยท์™ธ ์˜จ์‹ค๊ฐ€์Šค ์ œ๋„ 11 3.2 ๊ตญ๋‚ด ์—ฐ์•ˆํ•ด์šด ํ˜„ํ™ฉ 31 3.3 ์—ฐ์•ˆํ•ด์šด๊ณผ ์˜จ์‹ค๊ฐ€์Šค ๋Œ€์‘ ๋ฌธ์ œ์  35 ์ œ 4 ์žฅ ์‹ค์ฆ๋ถ„์„ 41 4.1 ๋ถ„์„ ๋ฐฉ๋ฒ• 41 4.2 ๋ถ„์„๋Œ€์ƒ 41 4.3 ์—๋„ˆ์ง€ ์†Œ๋น„ ๋ฐ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ ํ˜„ํ™ฉ 52 4.4 ์‹ค์ฆ๋ถ„์„ 58 ์ œ 5 ์žฅ ๊ฒฐ๋ก  61 ์ฐธ๊ณ ๋ฌธํ—Œ 63Maste

    ํ•œ๊ตญ์˜ ๊ฐ€์กฑ์ฃผ์˜์™€ ์‹œ๋ฏผ์ฐธ์—ฌ: ๊ฐ€์ • ๋‚ด ์‚ฌํšŒ์  ์—ญํ• ์ด ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒํ•™๊ณผ, 2016. 8. ์ด์žฌ์—ด.๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€์ • ์˜์—ญ ๋‚ด ์‚ฌํšŒ์  ์—ญํ• ์ด ์ž๋ฐœ์  ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ฒ€ํ† ํ•œ๋‹ค. ๊ธฐ์กด์˜ ์—ฌ์„ฑ์˜ ์ •์น˜ ์ฐธ์—ฌ ๋‹ด๋ก ์€ ์ œ๋„๊ถŒ ๋‚ด ์ •์น˜ ์ฐธ์—ฌ ํ˜„์ƒ์— ๋Œ€ํ•œ ์ง€์ ์— ๋จธ๋ฌด๋ฅด๋ฉด์„œ, ์ด์˜ ๊ธฐ๋ฐ˜๊ณผ ์กฐ๊ฑด์ด ๋˜๋Š” ์š”์†Œ๋“ค์— ๋Œ€ํ•ด์„œ๋Š” ์ถฉ๋ถ„ํžˆ ์ฃผ๋ชฉํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ๋˜ํ•œ, ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์€ ์‹œ๋ฏผ์‚ฌํšŒ ๋‚ด๋ถ€์˜ ๊ฐ์ข… ๊ฒฐ์‚ฌ์ฒด๋“ค ๋‚ด๋ถ€์—์„œ์˜ ๊ด€๊ณ„ ๋ฐ ์ ˆ์ฐจ๊ฐ€ ์–ผ๋งˆ๋‚˜ ๋ฏผ์ฃผ์ ์ด๊ณ  ๊ณตํ‰ํ•œ๊ฐ€ ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•˜๊ณ  ์žˆ์ง€๋งŒ, ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ์žˆ์–ด ์„ฑ๋ณ„ ๋ถ„๋ฆฌ ์–‘์ƒ์„ ์ž๋ฃŒ๋ฅผ ํ†ตํ•ด ๋ณด์—ฌ์ค„ ๋ฟ ๊ตฌ์ฒด์ ์ธ ๊ธฐ์ œ๋ฅผ ๊ฒฝํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•˜์ง€๋Š” ๋ชปํ–ˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€์กฑ ์˜์—ญ ๋‚ด ์‚ฌํšŒ์  ์—ญํ• - ๋ฐฐ์šฐ์ž ์—ญํ• ๊ณผ ๋ถ€๋ชจ ์—ญํ• -์ด ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‚ดํŽด๋ณด๋˜, ๊ทธ๊ฒƒ์ด ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์–ด๋–ค ์–‘์ƒ์„ ๋ณด์ด๋Š”์ง€, ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์ฐจ๋ณ„์  ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ถ€๋ชจ ์—ญํ• ์˜ ๊ฒฝ์šฐ ์ž๋…€ ์—ฌ๋ถ€๋ฅผ ํ†ตํ•ด ์ธก์ •๋˜๋ฉฐ, ๋ฐฐ์šฐ์ž ์—ญํ• ์˜ ๊ฒฝ์šฐ ๋ฏธํ˜ผ, ๊ธฐํ˜ผ ๋“ฑ๊ณผ ๊ฐ™์ด ๊ฒฐํ˜ผ ์ง€์œ„๋ฅผ ํ†ตํ•ด ์ธก์ •๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฒฝํ—˜์  ๋ถ„์„์„ ์œ„ํ•ด 2012๋…„ ํ•œ๊ตญ์ข…ํ•ฉ์‚ฌํšŒ์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ–ˆ๋‹ค. ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์ˆ˜ํ–‰ํ–ˆ์œผ๋ฉฐ, ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์ฐจ๋ณ„์  ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๊ธฐ ์œ„ํ•ด ์ƒํ˜ธ์ž‘์šฉ ํ•ญ์„ ํˆฌ์ž…ํ–ˆ๋‹ค. ์ฃผ์š” ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋‘ ์ฐจ๋ก€์˜ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ ๋ฐฐ์šฐ์ž ์—ญํ• ์€ ๋™์ฐฝํšŒ ์ฐธ์—ฌ์™€ ์‚ฌํšŒ๋ด‰์‚ฌ ๋‹จ์ฒด ์ฐธ์—ฌ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ์— ์žˆ์–ด์„œ ๋ชจ๋‘ ์˜ํ–ฅ์„ ์ฃผ๋˜, ์‘๋‹ต์ž์˜ ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์ฐจ๋ณ„์ ์ธ ์˜ํ–ฅ๋ ฅ์„ ๋ฐœํœ˜ํ–ˆ๋‹ค. ๋‚จ์„ฑ ์‘๋‹ต์ž์˜ ๊ฒฝ์šฐ ๋ฐฐ์šฐ์ž ์—ญํ• ์ด ์—†๋Š” ๊ฒฝ์šฐ(๋ฏธํ˜ผ/์‚ฌ๋ณ„ ๋“ฑ) ์ด๋Š” ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ฐ˜๋ฉด ์—ฌ์„ฑ์˜ ๊ฒฝ์šฐ ๋ฐฐ์šฐ์ž ์—ญํ• ์ด ์žˆ๋Š” ๊ฒฝ์šฐ ์˜คํžˆ๋ ค ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ๋™์ผํ•œ ๋ฐฉ์‹์˜ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ ๋ถ€๋ชจ ์—ญํ•  ๋˜ํ•œ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์ฐจ๋ณ„์  ํšจ๊ณผ๋ฅผ ๋ณด์—ฌ์ค€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋ถ€๋ชจ ์—ญํ• ์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ๋ณ€์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋˜, ์˜ค์ง ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์ฐจ์ด๊ฐ€ ๊ณ ๋ ค๋˜์—ˆ์„ ๋•Œ์—๋งŒ ๊ทธ๋Ÿฌํ•œ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์˜์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฐ€์กฑ์ฃผ์˜์™€ ์ด์— ๋”ฐ๋ฅธ ๊ฐ€์ • ์˜์—ญ ๋‚ด ์‚ฌํšŒ์  ์—ญํ• ์˜ ํšจ๊ณผ๋ฅผ ํ†ตํ•ด ๊ฒฐ์‚ฌ์ฒด ๋‚ด ์„ฑ๋ณ„ ๋ถ„๋ฆฌ์˜ ๊ธฐ์ œ์˜ ๋‹จ๋ฉด์„ ๋ฐํžˆ๊ณ ์ž ํ–ˆ๋‹ค. ํ•œ๊ตญ์˜ ๊ฐ€์กฑ์ฃผ์˜์™€ ์ด์˜ ์‚ฌํšŒ์  ๊ฒฐ๊ณผ๋ฅผ ํƒ๊ตฌํ•œ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์ด ์˜ค๋Š˜๋‚  ๊ทธ ์ค‘์š”์„ฑ์ด ๋”์šฑ ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋Š” ๋˜ ๋‹ค๋ฅธ ๊ณต์  ์˜์—ญ- ์‹œ๋ฏผ ์ฐธ์—ฌ์˜ ์˜์—ญ์— ์ฃผ๋ชฉํ•˜์ง€๋Š” ๋ชปํ•œ ๊ฐ€์šด๋ฐ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€์กฑ ๋‚ด ์‚ฌํšŒ์  ์—ญํ• ์ด ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์ฐจ๋ณ„์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ์ ์„ ๋ณด์˜€๋‹ค. ๋‘˜์งธ, ์ž๋ฐœ์  ๊ฒฐ์‚ฌ์ฒด๋ฅผ ์ •์˜ํ•˜๋Š” ๋ฐ์— ์žˆ์–ด ๊ธฐ์™•์— ์ „์ œ๋˜๋Š” ๋ฒ”์ฃผ๋‚˜ ์œ ํ˜•์„ ์‚ฌ์šฉํ•˜๋Š” ๋Œ€์‹ ์— ์‹ค์ œ ์ž๋ฃŒ์—์„œ ๋“œ๋Ÿฌ๋‚˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ์œ ํ˜• ๊ตฌ๋ถ„์˜ ์ถ•์„ ๊ตฌ์„ฑํ•  ๊ฒƒ์„ ์‹œ๋„ํ•˜์˜€๊ณ , ์ด์— ๋”ฐ๋ผ ๊ฐ ๊ฒฐ์‚ฌ์ฒด ์œ ํ˜•์˜ ์‹œ๋ฏผ์  ๊ฒฐ๊ณผ๊ฐ€ ๊ฐ€์ง€๋Š” ์ค‘์š”์„ฑ๊ณผ ํ•จ๊ป˜ ๋ถ„์„ ๊ฒฐ๊ณผ์˜ ํ•จ์˜๋ฅผ ๋…ผ์˜ํ–ˆ๋‹ค.1. ์„œ๋ก  1 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ๋ฐ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 6 2.1 ๊ฒฐ์‚ฌ์ฒด์˜ ์œ ํ˜• ๋ถ„๋ฅ˜์™€ ์‹œ๋ฏผ์  ๊ฒฐ๊ณผ 6 2.2 ํ•œ๊ตญ์˜ ๊ฐ€์กฑ์ฃผ์˜์™€ ๊ณต๋™์ฒด ์ฐธ์—ฌ๋กœ์„œ์˜ ์‚ฌํšŒ๊ถŒ 16 3. ์ž๋ฃŒ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 24 3.1 ์ž๋ฃŒ 24 3.2 ์ธก์ • 25 3.3 ๋ฐฉ๋ฒ• 27 4. ๋ถ„์„ ๊ฒฐ๊ณผ 27 4.1 ์‘๋‹ต์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 27 4.2 ๊ฒฐํ˜ผ ์ง€์œ„๊ฐ€ ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 30 4.3 ๋ถ€๋ชจ ์ง€์œ„๊ฐ€ ๊ฒฐ์‚ฌ์ฒด ์ฐธ์—ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 37 5. ๊ฒฐ๋ก  ๋ฐ ํ† ๋ก  44 ์ฐธ๊ณ  ๋ฌธํ—Œ 49 ๋ถ€๋ก 59 Abstract 66Maste

    [๋™ํ–ฅ] ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ

    Get PDF
    โ… . ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ ์ •์ฑ… โ–ก ๊ณ ์šฉ๋…ธ๋™๋ถ€, 2016๋…„ ํ•˜๋ฐ˜๊ธฐ ๊ตฌ์ง์ž ์ง์—…ํ›ˆ๋ จ๊ณผ์ • 3,725๊ฐœ ์„ ์ •(2016.7.1.) โ–ก ๊ณ ์šฉ๋…ธ๋™๋ถ€, ์ฒญ๋…„ ์นœํ™” ์šฐ์ˆ˜ ๊ต์œกํ›ˆ๋ จ 800์„  ์„ ์ •ยท์ง€์›(2016.6.3.) โ–ก ์—ฌ์„ฑ๊ฐ€์กฑ๋ถ€, ์—ฌ์„ฑ์ƒˆ๋กœ์ผํ•˜๊ธฐ์„ผํ„ฐ ํ•˜๋ฐ˜๊ธฐ ์ง์—…๊ต์œกํ›ˆ๋ จ๊ณผ์ • 20๊ฐœ ์ถ”๊ฐ€ ์„ ์ •(2016.6.23.) โ…ก. ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ ํ†ต๊ณ„ โ—ˆ ์ž„๊ธˆ๊ทผ๋กœ์ž์˜ ๊ต์œกยทํ›ˆ๋ จ ์ฐธ์—ฌ ํ˜„ํ™ฉ: ๊ฒฝ์ œํ™œ๋™์ธ๊ตฌ์กฐ์‚ฌ ๊ทผ๋กœ ํ˜•ํƒœ๋ณ„ ๋ถ€๊ฐ€ ์กฐ์‚ฌ(2016.3.) ๊ฒฐ๊ณผ โ–ก 2016๋…„ 3์›” ํ˜„์žฌ ์ž„๊ธˆ๊ทผ๋กœ์ž๊ฐ€ ์ง์žฅ์—์„œ ์ง€๋‚œ 1๋…„๊ฐ„ ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ๊ต์œกยทํ›ˆ๋ จ์„ ๋ฐ›์€ ๋น„์œจ์€ 55.1%๋กœ ์ž‘๋…„ ๋™์›” ๋Œ€๋น„ 1.8%p ์ฆ๊ฐ€ โ–ก ์ฒญ๋…„์ธต(15~29์„ธ) ์ž„๊ธˆ๊ทผ๋กœ์ž ์ค‘ 2016๋…„ 3์›” ํ˜„์žฌ ์ง์žฅ์—์„œ ์ง€๋‚œ 1๋…„๊ฐ„ ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ๊ต์œกยทํ›ˆ๋ จ์„ ๋ฐ›์€ ๋น„์œจ์€ 56.2%๋กœ ์ž‘๋…„ ๋™์›” ๋Œ€๋น„ 2.7%p ์ฆ๊ฐ€ โ–ก ๊ณ ๋ น์ธต(55~79์„ธ) ์ž„๊ธˆ๊ทผ๋กœ์ž ์ค‘ 2016๋…„ 3์›” ํ˜„์žฌ ์ง์žฅ์—์„œ ์ง€๋‚œ 1๋…„๊ฐ„ ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ๊ต์œกยทํ›ˆ๋ จ์„ ๋ฐ›์€ ๋น„์œจ์€ 45.5%๋กœ ์ž‘๋…„ ๋™์›” ๋Œ€๋น„ 4.6%p ์ฆ

    Clinical characteristics and treatment outcomes of patients with macrolide-resistant Mycobacterium avium complex pulmonary disease: a systematic review and meta-analysis

    Get PDF
    BACKGROUND: Macrolide is a key drug in the treatment of Mycobacterium avium complex pulmonary disease (MAC-PD). Macrolide-resistant MAC is gaining importance, but there are little data in clinical characteristics and treatment outcomes of macrolide-resistant MAC-PD (MR-MAC-PD). METHODS: We performed a systematic review and meta-analysis of published studies reporting clinical characteristics and treatment outcomes of patients with MR-MAC-PD. Risk of bias was assessed using the modified Newcastle-Ottawa Scale. RESULTS: Nine studies (seven retrospective and two prospective) comprising 319 patients were identified through a database search. Around 73% were women, and 52% had the fibrocavitary form. Pooled sputum culture conversion rate after combined multiple antibiotics or surgical resection was 21% (95% confidence interval [CI], 14-30%), and the one-year all-cause mortality was 10% (95% CI, 5-20%). There was no significant difference in treatment outcomes between nodular bronchiectatic and fibrocavitary types. CONCLUSIONS: Even combination therapy with fluoroquinolone, aminoglycoside, and surgical resection, the treatment outcomes of MR-MAC-PD were poor. The investigation of new treatment modalities is urgent.ope

    [๋™ํ–ฅ] ์ง์—…๊ต์œก

    Get PDF
    โ… . ์ง์—…๊ต์œก ์ •์ฑ… โ–ก ๊ต์œก๋ถ€, 2๋‹จ๊ณ„ ์‚ฐํ•™ํ˜‘๋ ฅ ์„ ๋„๋Œ€ํ•™(LINC) ์œก์„ฑ์‚ฌ์—… ์ฐธ์—ฌ ๋Œ€ํ•™ ๋ฐœํ‘œ(2014.5.9.) โ–ก ๊ต์œก๋ถ€ยทํ•œ๊ตญ๋Œ€ํ•™๊ต์œกํ˜‘์˜ํšŒ, 2013๋…„ ์‚ฐ์—…๊ณ„ ๊ด€์  ๋Œ€ํ•™ ํ‰๊ฐ€ ๊ฒฐ๊ณผ ๋ฐœํ‘œ(2014.5.15.) โ–ก ๊ต์œก๋ถ€ยท๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€, ์ œ2ํšŒ ์ฒญ์†Œ๋…„ ๊ธฐ์ˆ ์ฐฝ์—…์˜ฌ๋ฆผํ”ผ์•„๋“œ ๊ฐœ์ตœ(2014.5.26.) โ–ก ๊ต์œก๋ถ€, EBS์ง„๋‹จ์ฝ”์นญ(EDT) ์‹œ๋ฒ” ์šด์˜(2014.6.23.) โ–ก ๊ต์œก๋ถ€ยท์‚ฐ์—…ํ†ต์ƒ์ž์›๋ถ€, ์‹œํ—˜์ธ์ฆ๊ธฐ๊ด€-๋งˆ์ด์Šคํ„ฐ๊ณ  ์‹œํ—˜์ธ์ฆ ๋งž์ถคํ˜• ์ธ์žฌ ์–‘์„ฑ ์—…๋ฌด ํ˜‘์•ฝ ์ฒด๊ฒฐ(2014.6.24.) โ–ก ๊ต์œก๋ถ€, 2014๋…„ ์ „๋ฌธ๋Œ€ํ•™ ์œก์„ฑ์‚ฌ์—… ์„ ์ • ๊ฒฐ๊ณผ ๋ฐœํ‘œ(2014.6.27.) โ–ก ๊ต์œก๋ถ€, 2014๋…„ ํ•™๋ถ€๊ต์œก ์„ ๋„๋Œ€ํ•™(ACE) ์œก์„ฑ์‚ฌ์—… ์„ ์ • ๊ฒฐ๊ณผ ๋ฐœํ‘œ(2014.6.30.) โ…ก. ์ง์—…๊ต์œก ํ†ต๊ณ„ โ—ˆ 2014๋…„ 6์›” ๋Œ€ํ•™ ์ •๋ณด ๊ณต์‹œ โ–ก 4๋…„์ œ ์ผ๋ฐ˜๋Œ€ํ•™์˜ ์‚ฐํ•™ํ˜‘๋ ฅ๋‹จํšŒ๊ณ„ ์ด์˜ˆ์‚ฐ์€ 7์กฐ 5,757์–ต ์›์œผ๋กœ ์ „๋…„(7์กฐ 2,236์–ต ์›) ๋Œ€๋น„ 4.87% ์ฆ๊ฐ€ โ–ก 2013๋…„๋„ 4๋…„์ œ ์ผ๋ฐ˜๋Œ€ํ•™์˜ ์›๊ฒฉ๊ฐ•์ขŒ๋Š” 938๊ฐœ๋กœ ์ „๋…„(771๊ฐœ) ๋Œ€๋น„ 21.7% ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ์ˆ˜๊ฐ•์ธ์›์€ 63,691๋ช…์œผ๋กœ ์ „๋…„(51,225๋ช…) ๋Œ€๋น„ 24.3% ์ฆ๊ฐ€ โ–ก 2014๋…„ ์‹ ์ž…์ƒ์˜ ์ถœ์‹ ๊ณ ๋“ฑํ•™๊ต๋ณ„ ๋น„์œจ์€ ์ผ๋ฐ˜๊ณ  78.0%(-1.4%P), ์ž์œจ๊ณ  9.2%(+1.7%P), ํŠน์ˆ˜๋ชฉ์ ๊ณ  4.5%(-0.1%P), ๊ธฐํƒ€ 4.1% ์ˆœ์ž„. โ–ก 2014๋…„๋„ ์ž…ํ•™์ž(335,971๋ช…) ์ค‘ ๊ธฐํšŒ๊ท ํ˜• ์„ ๋ฐœ ์ „ํ˜• ํŠน์„ฑํ™”๊ณ  ์กธ์—…์ž๋Š” ์ „์ฒด ์ž…ํ•™์ž์˜ 1.5%(-0.1p%), ํŠน์„ฑํ™”๊ณ  ์กธ์—… ์žฌ์ง์ž๋Š” 0.3%(+0.1%p)์ž„

    ํ–‰๋ณตํ•œ ์ฒญ๋…„์˜ ํŠน์ง•

    Get PDF
    ํ–‰๋ณตํ•œ ์ฒญ๋…„์˜ ๋น„์œจ์€ ์žฅ๋ž˜ ํฌ๋ง ์ง์—…์„ ๊ฒฐ์ •ํ•œ ์ง‘๋‹จ์—์„œ๋Š” 69.9%, ๊ฒฐ์ •ํ•˜์ง€ ๋ชปํ•œ ์ง‘๋‹จ์—์„œ๋Š” 59.9%์ด๋ฉฐ, ๊ตฌ์ฒด์ ์ธ ์ง„๋กœ๊ณ„ํš์„ ์‹ค์ฒœํ•˜๋Š” ์ง‘๋‹จ์—์„œ๋Š” 73.1%, ๊ทธ๋ ‡์ง€ ์•Š์€ ์ง‘๋‹จ์—์„œ๋Š” 58.0%์— ๊ทธ์นจ. ์„ฑ๊ฒฉ๋ณ„๋กœ๋Š” ์™ธํ–ฅ์ (71.6%)์ด๊ณ  ์ •์„œ์ ์œผ๋กœ ์•ˆ์ •(75.2%)๋˜์–ด ์žˆ์œผ๋ฉฐ, ์นœํ™”์ (72.7%)์ด๊ณ  ์„ฑ์‹ค(70.9%)ํ•˜๋ฉฐ ๊ฐœ๋ฐฉ์ (68.4%)์ธ ์„ฑ๊ฒฉ์„ ๊ฐ€์ง„ ์‚ฌ๋žŒ์ด ๋” ํ–‰๋ณตํ•จ. ๋ณธ์ธ์˜ ์›”ํ‰๊ท  ์†Œ๋“์ด 200๋งŒ ์› ๋ฏธ๋งŒ์ผ ๋•Œ๋Š” ํ–‰๋ณตํ•œ ์ฒญ๋…„์˜ ๋น„์œจ์ด 61.0%์ด๋‚˜ 200๋งŒ ์›~300๋งŒ ์› ๊ตฌ๊ฐ„์—์„œ๋Š” 70.2%๋กœ ์ฆ๊ฐ€ํ•จ. ๊ทธ๋Ÿฌ๋‚˜ 300๋งŒ ์› ์ด์ƒ(68.9%)์ด ๋˜๋ฉด ํ–‰๋ณต ์ˆ˜์ค€์ด ์†Œํญ ๊ฐ์†Œํ•จ. ๊ณ ๋“ฑํ•™๊ต ์ดํ•˜ ์กธ์—…์ž ์ค‘ ํ–‰๋ณตํ•œ ์ฒญ๋…„์˜ ๋น„์œจ์€ 58.7%, ์ „๋ฌธ๋Œ€ํ•™ ์กธ์—…์ž๋Š” 63.6%, 4๋…„์ œ ๋Œ€ํ•™ ์กธ์—…์ž 65.9%, ์ƒ์œ„ 30์œ„๊ถŒ 4๋…„์ œ ๋Œ€ํ•™ ์กธ์—…์ž๋Š” 71.9%๋กœ ํ•™๋ ฅ๊ณผ ํ•™๋ฒŒ์ด ๋†’์„์ˆ˜๋ก ๋” ํ–‰๋ณตํ•จ. ๋‚จ์ž๋Š” ๋งˆ๋ฅด๊ฑฐ๋‚˜(53.8%), ๋น„๋งŒ์ธ ์‚ฌ๋žŒ(57.5%)๋ณด๋‹ค ๊ณผ์ฒด์ค‘(68.5%)์ธ ๊ฒฝ์šฐ ํ–‰๋ณตํ•œ ์‚ฌ๋žŒ์ด ๋” ๋งŽ์€ ๋ฐ˜๋ฉด, ์—ฌ์ž๋Š” ๋งˆ๋ฅผ์ˆ˜๋ก ํ–‰๋ณต ์ˆ˜์ค€์ด ๋†’์Œ.Of the group of young people who have decided on their future occupation, 69.9% feel that they are happy, while the respective figure for the group of those without decided future occupation is 59.9%. In addition, of the young people who are taking actions to carry out their concrete career plans, 73.1% feel they are happy, while the respective figure for those not taking actions is only 58.0%. In terms of personal characteristics, those who are extroverted (71.6%), emotionally stable (75.2%), friendly (72.7%), diligent (70.9%), and open-minded (68.4%) are happier. In terms of income, 61.0% of young people whose average monthly wage is less than 2 million won are happy. This figure increases to 70.2% for those with income in the 2 to 3 million won level. However, among those with an income level of 3 million won or higher, the level of happiness decrease somewhat (68.9%). In terms of education, 58.7% of young people with high school diplomas or lower are happy, while 63.6% of those who graduated from two-year colleges, 65.9% of those who graduated from four-year colleges and 71.9% of those who graduated from the top 30 four-year colleges are happy. The figures indicate that happiness levels are proportional to education levels as well as to the ranking of the college attended. Young, overweight males are happier (68.5%) than slim (63.8%) or obese (57.5%) young males; however, among young females, the lighter the body weight, the higher the level of happiness

    ์ทจ์—…์ž์˜ ์•ˆ์ „์˜์‹ ๊ตญ์ œ๋น„๊ต

    Get PDF
    OECD 15๊ฐœ๊ตญ ์ค‘ ํ•œ๊ตญ์˜ ์•ˆ์ „ ์ค‘์‹œ๋„(41.2%)๋Š” 12์œ„, ์•ˆ์ „ ์ฒด๊ฐ๋„(68.8%)๋Š” 13์œ„๋กœ ๋‚ฎ์Œ. ์ง์—… ํŠน์„ฑ๋ณ„๋กœ๋Š” ์ง€์ ๋…ธ๋™์ž(47.1%)๋ณด๋‹ค ์œ„ํ—˜ ๋…ธ์ถœ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์œก์ฒด๋…ธ๋™์ž(42.7%)์˜ ์•ˆ์ „ ์ค‘์‹œ๋„๊ฐ€ ๋‚ฎ์Œ. ํ•™๋ ฅ๋ณ„๋กœ๋Š” ๊ณ ์กธ ์ดํ•˜(40.9%)๊ฐ€ ๋Œ€์กธ ์ด์ƒ(41.6%)๋ณด๋‹ค ์•ˆ์ „ ์ค‘์‹œ๋„๊ฐ€ ๋‚ฎ์Œ. ์†Œ๋“ ์ˆ˜์ค€๋ณ„๋กœ๋Š” ์ €์†Œ๋“์ธต(42.0%)์ด ๊ณ ์†Œ๋“์ธต(57.6%)๋ณด๋‹ค ์•ˆ์ „ ์ค‘์‹œ๋„๊ฐ€ 15.6%p ๋” ๋‚ฎ์Œ. ์•ˆ์ „ํ•œ ์‚ฌํšŒ๋ฅผ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ดˆ๏ผŸ์ค‘๏ผŸ๊ณ ๋ถ€ํ„ฐ ์•ˆ์ „ ๊ต์œก์„ ์ฒด๊ณ„์ ์œผ๋กœ ์‹ค์‹œํ•˜๊ณ , ์•ˆ์ „์— ๋Œ€ํ•œ ์บ ํŽ˜์ธ์„ ์ง€์†์ ์œผ๋กœ ํŽผ์ณ ์•ˆ์ „์˜์‹์„ ์ œ๊ณ ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•จ.Of the 15 OECD countries, Korea ranked 12 with regard to the degree of importance placed on security (41.2%) and 13 in terms of perceived security (68.8%). In terms of job characteristics, manual workers (42.7%) with a greater chance of being exposed to danger had a lower degree of importance placed on security compared to knowledge workers (47.1%). With regard to education level, people with a high school level of education or lower (40.9%) were less concerned about security compared with those with a college education or higher (41.6%). In terms of income level, the low income group of people (42.0%) was 15.6%p less concerned about security compared with those with high income levels (57.6%). In order to establish a safe and secure society, it is necessary to enhance security perceptions by means of systematic education on security, starting from elementary school and continuing through middle school to high school, as well as on-going security and safety campaigns

    Multicenter Testing of a Simple Molecular Diagnostic System for the Diagnosis of Mycobacterium Tuberculosis

    Get PDF
    Mycobacterium tuberculosis (MTB) is a communicable disease and still remains a threat to common health. Thus, early diagnosis and treatment are required to prevent the spread of infection. Despite the recent advances in molecular diagnostic systems, the commonly used MTB diagnostic tools are laboratory-based assays, such as mycobacterial culture, MTB PCR, and Xpert MTB/RIF. To address this limitation, point-of-care testing (POCT)-based molecular diagnostic technologies capable of sensitive and accurate detection even in environments with limited sources are needed. In this study, we propose simple tuberculosis (TB) molecular diagnostic assay by combining sample preparation and DNA-detection steps. The sample preparation is performed using a syringe filter with amine-functionalized diatomaceous earth and homobifunctional imidoester. Subsequently, the target DNA is detected by quantitative PCR (polymerase chain reaction). The results can be obtained within 2 h from samples with large volumes, without any additional instruments. The limit of detection of this system is 10 times higher than those of conventional PCR assays. We validated the clinical utility of the proposed method in 88 sputum samples obtained from four hospitals in the Republic of Korea. Overall, the sensitivity of this system was superior to those of other assays. Therefore, the proposed system can be useful for MTB diagnosis in limited-resource settings.ope
    • โ€ฆ
    corecore