The Evolution of Medical Student Competencies and Attitudes in Digital Health Between 2016 and 2022: Comparative Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Modern healthcare systems worldwide are facing challenges, and digitalization is viewed as a way to strengthen healthcare globally. As healthcare systems become more digital, it's essential to assess healthcare professionals' competencies and skills to ensure they can adapt to new practices, policies, and workflows effectively. OBJECTIVE: The aim of this study was to analyse how the attitudes, skills and knowledge of medical student concerning digital health have shifted from 2016 to 2022 in connection with the development of the national healthcare information system architecture utilising the Clinical Adoption Meta-Model framework. METHODS: The study population consisted of fifth-year medical students from one University in Finland during 2016, 2021 and 2022. A survey questionnaire was administered comprising seven background questions and 16 statements rated on a five-point Likert scale assessing students' attitudes towards digital health and their self-perceived digital capabilities. The results were recategorized into a dichotomous scale. The statistical analysis employed Pearson's chi-square test. The Benjamini-Hochberg procedure was used for multiple variable correction. RESULTS: The study included 215 medical students (n = 45 in 2016, n = 106 in 2021, and n = 64 in 2022) with an overall response rate of 53% (43% in 2016, 74% in 2021, and 42% in 2022). Throughout 2016, 2021, and 2022, medical students maintained positive attitudes towards using patient-generated information and digital applications in patient care. Their self-perceived knowledge of the national patient portal significantly improved, with agreement increasing by 35 percentage points from 2016 to 2021 (P<.001) and this trend continued in 2022 (P<.001). However, their perceived skills in using electronic medical records did not show significant changes. Additionally, students' perceptions of the impact of digitalization on health promotion improved markedly from 2016 to 2021 (with agreement rising from 53% to 78%, P=.002) but declined notably again by 2022. CONCLUSIONS: Medical students' attitudes and self-perceived competencies have shifted over the years, potentially influenced by the national health information system architecture developments. However, these positive changes have not followed a completely linear trajectory. To address these gaps, educational institutions and policymakers should integrate more digital health topics into medical curricula and provide practical experience with digital technologies to keep professionals up-to-date with the evolving healthcare 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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».