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Record W2915157966 · doi:10.3917/sim.184.0031

Factors Affecting the Adoption of Connected Objects in e-Health: A Mixed Methods Approach

2019· article· fr· W2915157966 on OpenAlex
Vincent Dutot, François Bergeron, Kristina Rozhkova, Nicolas Moreau

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

VenueSystèmes d information & management · 2019
Typearticle
Languagefr
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversité TÉLUQ
Fundersnot available
KeywordsHumanitiesPolitical scienceEthnologyArtSociology

Abstract

fetched live from OpenAlex

Les objets connectés offrent une perspective nouvelle pour l’e-santé et l’économie. Cependant, les facteurs d’adoption de l’e-santé ou des objets connectés restent peu étudiés et compris. Cette recherche aborde les facteurs d’adoption des objets connectés dans l’e-santé en s’appuyant sur la combinaison successive de méthodes de recherche qualitative et quantitative. A partir d’entrevues semi-dirigées, un modèle de recherche est développé et testé auprès de 226 professionnels de la santé (par enquête en ligne). Les résultats de cette méthodologie mixte indiquent les rôles primordiaux de l’influence sociale et la commodité perçue dans l’adoption. Cinq autres facteurs contribuent, dans une mesure moindre à l’adoption : la compatibilité, l’interopérabilité, l’intégration, la capacité de démonstration des résultats et la réputation. Cette recherche offre une contribution importante et propose de nouvelles avenues pour assurer le lancement d’objets connectés dans l’e-santé.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.073
GPT teacher head0.371
Teacher spread0.297 · 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