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Record W3028892374

뇌졸중 환자의 편의성과 안전성을 고려한 목욕의자 개발 및 평가

2019· article· ko· W3028892374 on OpenAlexaboutno aff
손영효, 백지영, 정현애, 김희동, 박제모, 황도연, Ji Hun Lee, 송종운, 정화식

Bibliographic record

VenueThe Journal of Korean Society of Occupational Therapy · 2019
Typearticle
Languageko
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityRehabilitationTest (biology)Physical therapyDelphi methodPsychologyMedicineComputer scienceHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

Objective: The purpose of this study was to develop a bath chair and verify its effectiveness in terms of the convenience and safety of stroke patients. Methods: The subjects were 20 stroke patients admitted to a rehabilitation hospital in a metropolitan city. As a research tool, a bath chair prototype was developed based on a Delphi survey and subjective opinions from the subjects. A Rapid Entire Body Assessment (REBA), questionnaire (usability assessment and open questionnaire), and Korean version of Quebec User Evaluation of Satisfaction Assistive Technology (K-QUEST 2.0) were used as assessment tools. As a statistical analysis, a t-test was conducted before and after applying the bath chair prototype. Results: As the results from before and after using the bath chair prototype indicate, the REBA levels of action regarding the use of bath ware and a showerhead are lowered from ‘high’ to ‘normal’ and ‘low,’ respectively. In addition, in the usability test, convenience was considered to be ‘proper’ and seven other items expressed values of ‘normal’. In addition, the prototype was deemed ‘satisfactory’ in terms of effectiveness, whereas seven other indicators showed a level of ‘normal’. Conclusion: The bath chair developed in this study is expected to not only be effective in improving the convenience and safety of the bathing activity of stroke patients but also to serve as an assistive device to reduce their physical burden.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.170
GPT teacher head0.401
Teacher spread0.231 · 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 teacher head, not a consensus.

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Korean Society of Occupational TherapySame topicDiverse Approaches in Healthcare and Education StudiesFrench-language works237,207