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Record W2419190659 · doi:10.3233/978-1-61499-574-6-26

The Consumer Health Information System Adoption Model

2015· article· en· W2419190659 on OpenAlex
Helen Monkman, André Kushniruk

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudies in health technology and informatics · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordseHealthUsabilityHealth literacyLiteracyKnowledge managementBusinessAffect (linguistics)MarketingComputer sciencePsychologyHealth careHuman–computer interaction

Abstract

fetched live from OpenAlex

Derived from overlapping concepts in consumer health, a consumer health information system refers to any of the broad range of applications, tools, and educational resources developed to empower consumers with knowledge, techniques, and strategies, to manage their own health. As consumer health information systems become increasingly popular, it is important to explore the factors that impact their adoption and success. Accumulating evidence indicates a relationship between usability and consumers' eHealth Literacy skills and the demands consumer HISs place on their skills. Here, we present a new model called the Consumer Health Information System Adoption Model, which depicts both consumer eHealth literacy skills and system demands on eHealth literacy as moderators with the potential to affect the strength of relationship between usefulness and usability (predictors of usage) and adoption, value, and successful use (actual usage outcomes). Strategies for aligning these two moderating factors are described.

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.

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)0.005
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.121
GPT teacher head0.457
Teacher spread0.336 · 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