Evolving Medical Students’ Digital Health Perceptions and Intentions: Insights From a Prepandemic and Postpandemic Survey Study
Notice bibliographique
Résumé
Background: Digital health (dHealth) technologies, such as telehealth, artificial intelligence (AI), and mobile apps, are increasingly essential in medical practice. However, despite their growing significance, medical curricula often lack structured dHealth training, leaving students underprepared for digitally integrated health care environments. Objective: This study investigates the factors influencing medical students' intentions to integrate dHealth technologies into their future practice and examines changes in their perceptions over time. Methods: We conducted a 2-phase survey at a large Canadian medical school to assess changes in perceptions before (N=184) and after (N=177) the COVID-19 pandemic. A mixed methods approach combined component-based structural equation modeling and fuzzy-set qualitative comparative analysis. The model was grounded in the technology acceptance model and Triandis' theory of interpersonal behavior, examining constructs such as individual background, facilitating conditions, perceived usefulness, and beliefs about AI. Results: Across both phases, over 85% (306/361) of students agreed that dHealth education should be a mandatory component of medical training. Mean ratings for intention to use dHealth in future practice increased significantly between t0 and t1 for patient communication (3.4 to 4.2, P<.001), monitoring (3.3 to 4.0, P<.001), and diagnosis/treatment (3.6 to 4.2, P<.001). Experience with AI tools increased from 1.3 to 1.5 (P<.001), and telehealth from 1.2 to 1.6 (P<.001), while exposure to hospital IT systems and mobile apps remained unchanged. Results confirmed that perceived usefulness (β=.37 at t0; β=.34 at t1) and beliefs about AI (β=.39 at t0; β=.27 at t1) were strong predictors of intention to integrate dHealth (P<.001). The explanatory power of the structural equation modeling model declined postpandemic (R²=0.53 at t0 vs R²=0.25 at t1), suggesting increasing complexity in influencing factors. Fuzzy-set qualitative comparative analysis revealed multiple configurations leading to high intention, with consistency values exceeding 0.88 and overall solution coverage of 0.74 postpandemic. Core conditions across high-intention profiles included strong beliefs in the role of AI and perceived importance of dHealth education. Conversely, gender appeared as a recurring core condition in non-high-intention configurations, suggesting persistent disparities in dHealth adoption. Conclusions: The study advocates for the integration of formal dHealth training in medical curricula to better prepare future physicians for the demands of an increasingly digital health care landscape. While the COVID-19 pandemic may have contributed to shifting perceptions, other factors, such as recent AI advancements, likely played a role. These findings highlight the urgent need for medical education to adapt to the changing dHealth environment.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».