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Record W1592963321 · doi:10.1002/meet.14505001034

Design and evaluation of web interfaces for informal care providers in senior monitoring

2013· article· en· W1592963321 on OpenAlexafffundabout
Lu Xiao, Xueheng Yan, Alison Emery

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFormative assessmentUsabilityHeuristic evaluationPreferenceInterface (matter)Work (physics)Computer sciencePsychologyHuman–computer interactionKnowledge managementWorld Wide WebInternet privacyEngineering

Abstract

fetched live from OpenAlex

Abstract It has been recognized that most seniors prefer to age in a place with familiar surroundings until their health makes this impossible. In an attempt to address the aging phenomenon, as well as recognize seniors' preference, we worked with a Canada‐based company that develops a sensor‐based home monitoring system for people to monitor the home activities of independently living seniors. Our role was to develop web interfaces that present sensor data to the intended web users – the seniors' informal care providers (e.g., their close friends or family members). In this paper, we present the information design and the web interface prototypes, and report the results of our formative evaluations through cognitive walkthrough and heuristic evaluation methods. The common problems discovered in both methods were problematic notification mechanism, inconsistency, background and layout. Each method also detected usability issues that the other did not. Our work adds more empirical evidence to the importance of combining evaluation methods in a study. The experiences in this study also helped us reflect on approaches and strategies when working with industry partners.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.003
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 head0.021
GPT teacher head0.309
Teacher spread0.287 · 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.

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

Citations11
Published2013
Admission routes3
Has abstractyes

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