Factors Affecting the Adoption of Connected Objects in e-Health: A Mixed Methods Approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".