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Record W4386937860 · doi:10.1007/978-3-031-43950-6_16

Effectiveness of a Korean Smart Home Modification Program: Focused on People with Physical Disabilities

2023· book-chapter· en· W4386937860 on OpenAlexaboutno aff
KwangTae Moon, Yeong-hun Kwon

Bibliographic record

VenueLecture notes in computer science · 2023
Typebook-chapter
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsActivities of daily livingQuality of life (healthcare)Intervention (counseling)Occupational therapyGerontologyIndependent livingHome automationPsychologyMedicinePhysical therapyComputer scienceNursingTelecommunications

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to investigate the effects of the Korean Smart Home Modification Program (KSHMP) on the activities of daily living and health-related quality of life of people with physical disabilities. The study used a single-group pre-post design with 10 people with physical disabilities living at home. During the intervention period, the KSHMP was implemented, which included occupational profile, smart home installation, setup, training, task-based feedback, and monitoring. Post-intervention changes in activities of daily living and health-related quality of life were assessed with the Canadian Occupational Performance Measure (COPM) and EuroQual-5 Dimensions (EQ-5D). After the KSHMP, all 10 subjects improved their activities of daily living and quality of life. In addition, the occupational performance of all subjects was maintained. These results show that a customized smart home has a positive impact on improving the activities of daily living and quality of life of people with retardation and is an efficient alternative.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.381
Teacher spread0.323 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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