MétaCan
Menu
Back to cohort
Record W2952489929 · doi:10.4324/9781315638386-4

Health-related technology use by older adults in rural and small town communities

2016· book-chapter· en· W2952489929 on OpenAlexaff
Beth Perry, Margaret Edwards, Carol L. Anderson, Maiga Chang, Dr Kinshuk, Pamela Hawranik

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of ManitobaAthabasca UniversityAlberta Health Services
Fundersnot available
KeywordsGerontologyGeographySocioeconomicsMedicineSociology

Abstract

fetched live from OpenAlex

This chapter explores how older adults living in rural and small town communities use technology for health-related purposes to remain healthy and live independently. Specifically, it explores if, and how, this population uses technology for health-related education, social support, health reminders, health alerts and health parameter monitoring. The study is foundational to addressing questions related to effective use of technology for improving health self-care self-efficacy and maintenance of ability to independently perform activities of daily living in older adults. Data gathered include ways older adults use technology for health-related purposes, access they have to health-related technology and ways older adults consider health-related technology helpful in assisting them to remain independent and community-dwelling. A review of background literature, description of the research methods used, study findings and a discussion including recommendations for enhancing the use of health-related technology by older adults is included. The chapter concludes with recommendations for further research.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.880
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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 designNot applicable
Domainnot available
GenreOther

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

Explore more

Same topicBiomedical and Engineering EducationFrench-language works237,207