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Record W2693103740 · doi:10.1093/ppar/prx007

Aging and Technology: Taking the Research into the Real World

2017· article· en· W2693103740 on OpenAlexaffabout
Andrew Sixsmith, Alex Mihailidis, Dorina Simeonov

Bibliographic record

VenuePublic Policy & Aging Report · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsToronto Rehabilitation InstituteOntario Stroke NetworkSimon Fraser University
Fundersnot available
KeywordsPsychologyBusiness

Abstract

fetched live from OpenAlex

Information and communication technologies (ICTs) are increasingly being used to support the health, well-being, and independence of seniors, and to open up business opportunities for service providers and industry (Sixsmith, 2013). The use of ICTs may also provide new solutions to the increased demands on health services that result from rising service costs and population aging. Most importantly, ICTs can be used to extend the care and support options in the community to meet the desire of most seniors to age in place (Sixsmith and Sixsmith, 2008). Despite this potential, the actual social and economic impact of research has so far been limited. This paper looks at how (Aging Gracefully across Environments using Technology to Support Wellness, Engagement, and Long Life NCE Inc.) AGE-WELL, a Canadian Network of Centres of Excellence (http://www.agewell-nce.ca), aims to take the outcomes of research from the laboratory into real-world devices and applications.

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)0.010
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.006
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.001
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 head0.095
GPT teacher head0.441
Teacher spread0.346 · 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; both teacher heads agree on what is shown here.

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

Citations25
Published2017
Admission routes2
Has abstractyes

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