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Record W2128263257 · doi:10.1145/2702123.2702430

"My Hand Doesn't Listen to Me!"

2015· article· en· W2128263257 on OpenAlexaff
Bárbara Barbosa Neves, Rachel L. Franz, Cosmin Munteanu, Ronald M. Baecker, Mags Ngo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUsabilityDigital literacyLiteracyPopulationInternet privacyInformation and Communications TechnologyPsychologyComputer scienceWorld Wide WebHuman–computer interactionSociology

Abstract

fetched live from OpenAlex

Adoption and use of novel technology by the institutionalized 'oldest old' (80+) is understudied. This population is the fastest growing demographic group in developed countries, providing design opportunities and challenges for HCI. Since the recruitment of oldest old people is challenging, research tends to focus on older adults (65+) and their use of and attitudes towards existing communication technologies, or on their caregivers and social ties. Our study deployed a novel communication appliance among five frail oldest old people living in a long-term care facility, which included field observations and usability and accessibility tests. Our findings suggest factors that facilitate and hinder the adoption of communication technologies, such as social, attitudinal, digital literacy, physical, and usability. We also discuss issues that arise in studying technology adoption by the oldest old, including usability and accessibility testing, and suggest solutions that may be helpful to HCI researchers working with this population.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.009

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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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 designQualitative
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

Citations108
Published2015
Admission routes1
Has abstractyes

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Same topicTechnology Use by Older AdultsFrench-language works237,207