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Record W2398027180

Problems in Using Home Appliances by Elderly Persons Who Live Alone in Japan, and Some Design Proposals

2015· article· en· W2398027180 on OpenAlexaboutno aff
Jiro Sagara, Rumi Tanemura, Kazue Noda, Toru Nagao

Bibliographic record

VenueInternational Convention on Rehabilitation Engineering & Assistive Technology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministryQuarter (Canadian coin)GerontologyWelfareElderly peoplePensionEveryday lifePopulationPopulation ageingMedicinePsychologyEnvironmental healthBusinessGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Japan is well known as the country which has highest population ratio of the elderly (65+) in the world. It exceeded 25% in 2014. In 2013, the Ministry of Health, Labor and Welfare of Japan announced that there are 4.62 million dementias and about 4 million MCIs in Japan. That means about quarter of elderly have some cognitive problems in their daily life. Adding this, the family size of Japan become so smaller that 9.4% of houses were solitary household by over 65 in 2010, and is growing up rapidly. The authors interviewed 183 elderlies including dementias who live alone in own home or stay alone in daytime, using the ETUQ-Kobe method which is based on Everyday Technology Usage Questionnaire developed by Louise Nygard in Karolinska Institute Sweden. Through this survey, the authors found a lot of problems disturbing their independent based on their memory loss. The relatives or care givers tend to take up their activity or role in home after some mistakes happen. Then the independency drop in worth spiral. In this paper, the authors describe such problems and some design proposals to support their independent life.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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.

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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Convention on Rehabilitation Engineering & Assistive TechnologySame topicTechnology Use by Older AdultsFrench-language works237,207