Behaviour Change and e-Health – Looking Broadly: A Scoping Narrative Review
Notice bibliographique
Résumé
Behaviour change can refer to any transformation or modification of human behaviour. Within healthcare it refers to a broad range of activities and approaches that focus on the individual, community, or environmental influences on health-related behaviour. For e-Health (or digital health) it refers to behavioural impacts mediated through a specific e-Health intervention. However, there are also other health-related behaviour changes being quietly imposed upon both the populace and the healthcare professions broadly, by use of information and communications technologies for health. To better understand these deliberate or incidental impacts on the behaviour of healthcare consumers and providers alike, a scoping narrative review was performed using peer-reviewed and grey literature resources. Qualitative information was charted from the selected literature. This created an objective analysis of both contemporary and less commonly appreciated aspects of behaviour change in our 'digital' age. Many contemporary examples exist. The Internet and www brought alternate approaches moving from face-to-face or paper-based to websites, electronic diaries, and now mobile phones (particularly smartphones) to personalize health-related behaviour change in a myriad of diseases and conditions. Segments of the population have also exhibited health-related behaviour change through their growing www-based health-information seeking. More recent examples include 'spontaneous telemedicine' where physicians have changed the behaviour of themselves and colleagues through use of Instant Messaging, e.g., WhatsApp. Patients are also changing their behaviour spontaneously through taking and providing 'medical selfies'. However, the recent and rapid growth in accessibility and popularity of social media has markedly impacted behaviour change through the speed with which information can be spread, by both legitimate users and socialbots. Insidious examples include spread of health-related 'misinformation' (e.g., vaginal cleansing,), and now 'disinformation' (e.g., the 'anti-vaccination' movement, now resulting in recurrence of once eradicated diseases). These, and other examples, represent the broader, sometimes incidental, impact of some current e-health approaches on health-related behaviour change and should be identified and acknowledged as such. Doing so may fundamentally change opinion and efforts to redirect elements of behaviour change and aspects of behaviour change theory in unexpected ways.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».