Understanding acceptability of digital health technologies among francophone-speaking communities across the world: a meta-ethnographic study
Notice bibliographique
Résumé
Introduction Increasingly, people are turning toward digital health technologies to support their care management, communication with health professionals, and performing activities of daily living. Digital health technologies may be well implemented in clinical practices in several jurisdictions, but the influence of sociocultural factors may sometimes be neglected. To increase use and sustainability of these innovative solutions in health care, we need to understand acceptability among diverse groups of the population such as linguistically diverse populations. Francophone-speaking populations in Canada, for example, are known to endure challenges with income, health and difficulties associated with living in rural areas which impede on their likelihood to use digital health technologies. As part of the University of Ottawa International Francophonie Research Chair on Digital Health Technologies, this study aimed to understand the conditions that make digital health technologies acceptable among francophone-speaking communities. Methods Using a meta-ethnography methodology, this study synthesizes international qualitative research on social acceptability of digital health technology among francophone-speaking communities. We focused on four types of digital health technologies: telemedicine, mobile technologies, wearable technologies, and robotic technologies. Using Noblit and Hare's 7 phase approach to conducting a meta-ethnography, we were able to get a comprehensive synthesis and understanding of the research landscape on the issue. Studies published between 2010 and 2020 were included and synthesized using NVivo, excel and a mind mapping technique. Results Our coding revealed that factors of social acceptability for digital health technologies could be grouped into the following categories: care organization, self-care support, communication with care team, relational and technical risks, organizational factors, social and ethical values. Our paper discusses the themes evoked in each category and their relevance for the included digital health technologies. Discussion In discussing the results, we present commonalities and differences in the social acceptability factors of the different digital health technologies. In addition, we demonstrate the importance of considering sociocultural diversity in the study of social acceptability for digital health technologies. Implications The results of this study have implications for practitioners who are the instigators of digital health technology implementation with healthcare service users. By understanding factors of social acceptability among francophone-speaking communities, practitioners will be better suited to propose and support the implementation of technologies in ways that are suitable for these individuals. For policymakers, this knowledge could be used for developing policy actions based on consideration for diversity.
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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 | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».