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Record W2478539091

QUEST를 이용한 보조기(orthoses) 사용 만족도 평가

2016· article· ko· W2478539091 on OpenAlexaboutno aff
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Bibliographic record

Venue한국산학기술학회논문지 · 2016
Typearticle
Languageko
FieldHealth Professions
TopicInnovation in Digital Healthcare Systems
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

본 연구는 장애인의 보조기 사용에 대한 만족도와 중요도 분석을 통해 소비자 중심의 보조기 서비스 개선방안을 제시하고자 실시하였다. 보조기를 사용하는 장애인 185명을 대상으로 QUEST(Quebec User Evaluation of Satisfaction with assistive Technology)를 이용한 설문조사를 실시하였고, 보다 심도깊은 논의를 위하여 추가로 25명을 대상으로 심층면접을 실시하였다. 보조기 기구 만족도는 3.78점, 보조기 서비스 만족도는 3.52점으로 전체 만족도는 3.68점이 나왔으며, 개별 항목에서 만족도는 효과성이 가장 높았고, 가격이 가장 낮게 나타났다. 만족도 개별항목의 중요도에서는 가격, 안락함, 수리서비스, 그리고 사후관리 등을 중요한 항목으로 선택하였다. 건강보험 등 공적급여제도의 보조기 수가체계는 현실을 반영한 합리적 개선이 필요하며, 수리를 포함한 사후관리 등의 전달체계 구축이 필요하다고 할 수 있다.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.449
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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