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Enregistrement W4360999314 · doi:10.3389/fdgth.2023.1165504

Editorial: Education and learning for digital health

2023· editorial· en· W4360999314 sur OpenAlexaboutno aff
Elizabeth Morrow, Fiona Ross, Cindy Mason

Notice bibliographique

RevueFrontiers in Digital Health · 2023
Typeeditorial
Langueen
DomainePsychology
ThématiqueDigital Mental Health Interventions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer scienceDigital learningData scienceMathematics educationPsychologyMultimedia

Résumé

récupéré en direct d'OpenAlex

At a time of rapid digital innovation, Education and Learning for Digital Health sparks novel thinking and insights into how and why health professionals learn to use emerging technologies. The research topic builds on established educational practices and associated literatures on e-learning, blended-learning, immersive virtual reality, digital simulations and virtual patients. See for example, the Frontiers research topic Advancing Teaching and Learning in Health Sciences Across Healthcare Professionals. Many robust educational resources and tools for digital health have been developed, including Health Education England’s e-learning for health (https://www.e-lfh.org.uk/). The COVID-19 pandemic impelled further innovation in health professional’s online learning, as education providers worked tirelessly to avoid disruption to learning, as captured in Frontiers research topic Impact of COVID-19 on Healthcare Professions Education.The articles, which have been produced by 17 world-leading clinicians, researchers and educators from Australia, Canada, United States and Republic of Ireland, together with the expertise of article editors and peer reviewers from Finland, Netherlands, Ireland, Australia, UK, USA, and Canada, advance the scientific paradigm of the discipline in four specific areas:• Health professionals learning to adapt and use virtual care (VC)• Digital professionalism in the use of smartphone technologies• Preparing medical students to use artificial intelligence (AI) and machine learning (ML)• Safe use of virtual reality (VR) technologies in professional education It is significant that the international collaborations, studies and articles of this research topic were all produced during the pandemic. In effect, this context ignited digital health by necessity, in order to maintain clinical care during social distancing and infection control protocols, while safeguarding human rights and preserving the ethics of healthcare.For example, in the first article, authors Vernon Curran, Ann Hollett and Emily Peddle explain how the use of virtual care, such as virtual examinations, clinical assessments and remote patient monitoring, expanded during COVID-19 to enable continued access to healthcare. Their survey study Virtual Care and COVID-19: A Survey Study of Adoption, Satisfaction and Continuing Education Preferences of Healthcare Providers in Newfoundland and Labrador, Canada provides insights into healthcare providers’ experiences during the unfolding pandemic. It demonstrates that not all VC methods were perceived to deliver the same quality of care that would be expected in traditional face-to-face clinical encounters, but that there can be other advantages for maintaining a virtual proximity to patients whilst minimising infection transfer risk. As a result, the authors recommend healthcare provider organisations ensure VC is backed up with Continuing Professional Development (CPD), guidelines, and resources including patient educational support. The second article focuses on the legal and ethical dimensions of smartphone technology. From Republic of Ireland, Bernadette John, Christine McCreary and Anthony Roberts authored Smartphone Technology for Clinical Communication in the COVID-19 Era: A Commentary on the Concerning Trends in Data Compliance. They argue that smartphone technologies afforded clinicians and patients many observed advantages during COVID-19, yet the longterm use of such devices needs to be compliant with protecting patient data security and privacy. Solutions offered include healthcare institutional guidelines, supportive digital professionalism training, and education opportunities. The authors of the third article in this collection, suggest that changes to support future healthcare should begin in medical schools. From the United States, authors Timothy Frommeyer, Reid Fursmidt, Michael Gilbert and Ean Bett elaborate on The Desire of Medical Students to Integrate Artificial Intelligence Into Medical Education: An Opinion Article. They draw on their wealth of experience in precision medicine, drug discovery, diagnostics, and hospital administration to argue that the advancement of AI and machine learning algorithms are reshaping the way physicians and healthcare providers approach the practice of medicine. They call for medical schools across the world to take up their essential educational role to ensure that changes to healthcare are for the better and that future physicians will be more competent, inventive, and compassionate in the medicine of tomorrow. The fourth article considers how, alongside changes in curricula content, advance technologies are changing the modes of education delivery to provide digitally enhanced learning experiences. From Australia, authors Nathan Moore, Kathy Dempsey, Peter Hockey, Susan Jain, Philip Poronnik, Ramon Shaban and Naseem Ahmadpour explain their work on Innovation During a Pandemic: Developing a Guideline for Infection Prevention and Control to Support Education Through Virtual Reality. Their article focuses on virtual reality as an educational technology with the ability to deliver flexible and immersive education. Their attention to safe infection control practices of VR head-worn display systems is ensuring safer transfer between clinicians.The insights from these articles show that education and learning for digital health needs to address a growing range of patient rights, professional practice, and governance issues. The issues extend from the level of individual practitioner’s use of technologies protecting patient confidentiality; to institutional policies, data licencing, copyright agreements and intellectual property rights; to whole health system design and regulation of digital health technologies; as well as raising public awareness and trust in such advances (Zidaru et al., 2021).Looking to the future, it is through the combination of education and learning in humans and machines that new knowledge will gain greatest power to maximise well-being, as explored in the related Frontiers research topic The Good Side of Technology: How We Can Harness the Positive Potential of Digital Technology to Maximize Well-being. The design and use of advanced technologies in healthcare is increasingly looking beyond hybrid and ‘human in the loop’ models, towards the symbiosis of human-AI intelligent caring in healthcare design, resourcing, evaluation, and improvement. This new values-based approach to technology development we propose (Morrow et al., 2023) reinforces the values established in professional practice through technologies themselves. For example, new technologies are being developed with Artificial Compassion design methods and tools (Mason, 2023). The idea, first developed by co-editor and AI technologist Cindy Mason (Mason, 2015) takes the science and wisdom of human compassion and embeds it into technologies and algorithms to enhance human lives. The present innovative digital health landscape creates an opportunity to rapidly advance human-AI intelligent caring through enhanced educational curricula and transformative learning experiences.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,295
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,386
Écart entre enseignants0,369 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2023
Routes d'admission1
Résumé présentoui

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