Editorial: Integrating digital health technologies in clinical practice and everyday life: unfolding innovative communication practices
Notice bibliographique
Résumé
These technologies also provide access to tailored educational resources and enhanced health communication strategies. At the same time, their use presents complex social, organizational, communicational, and interactional challenges. Such challenges include how to build constructive relationships with and through technology and how to improve health communication to engage people in self-care practices or limit possible physical, psychological, or emotional harms for patients. Broadly speaking, the integration of digital health technologies into clinical practice and the daily lives of patients thus remains a major challenge for health organizations.The eight articles featured in this special issue focus on various communication practices related to the use of digital health technologies by patients and healthcare providers. Three articles focus on the transformations of patient-provider communication and relationships during technology-enabled consultations and treatment. Using a multi-modal conversation analysis approach, Dalmeijer and colleagues examine the role of digital technology in interactions between occupational therapists (OTs) and parents of infants and toddlers with cerebral palsy taking part in a pediatric rehabilitation program. Stumpël and colleagues conducted a qualitative interview study to explore the perspectives of health care professionals in neonatal intensive care units on the impact of webcams on communication with parents and family-centred care. Branley et al.'s experimental study examines patients' preferences for consultations with physicians or chatbots when seeking advice for embarrassing and stigmatizing conditions. Three articles address new forms of interactions between health care professionals enabled by technology. Trupia and colleague's qualitative interview study describes the various uses of tele-expertise in dermatology and explores the dermatologists' perspectives on virtual interactions with their colleagues to produce a diagnostic opinion at a distance. Weiste et al. use conversation analysis to study how professionals involved in return-to work negotiations use meeting memos to facilitate opportunities for participation during virtual meetings. Mlynár and colleagues' ethnomethodological/conversation analysis study reports on interactions between physicians and medical radiology technicians when they were learning to use an artificial intelligence medical imaging platform.Finally, two articles raise issues related to the acceptability of digital health technologies and explore solutions to support their implementation and use. Gauthier-Beaupré and Grosjean present a meta-ethnographic review on the social acceptability of digital health technologies by French-speaking minority communities in Canada. Davat and colleague's study explores the aspirations and challenges encountered by health care providers, patients, technology designers, and researchers when employing participatory design methodologies to develop monitoring devices for heart failure.We would like to thank all the authors for their contributions to this special issue. Thank you also to all the reviewers who have supported the peer review process.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».