Early adopters or laggards? Attitudes toward and use of social media among urologists
Notice bibliographique
Résumé
OBJECTIVE: To understand the attitudes and practices of urologists regarding social media use. Social media services have become ubiquitous, but their role in the context of medical practice is underappreciated. SUBJECTS AND METHODS: A survey was sent to all active members of the Canadian Urological Association by e-mail and surface mail. Likert scales were used to assess engagement in social media, as well as attitudes toward physician responsibilities, privacy concerns and patient interaction online. RESULTS: Of 504 surveys delivered, 229 were completed (45.4%). Urologists reported frequent or daily personal and professional social media use in 26% and 8% of cases, respectively. There were no differences between paper (n = 103) or online (n = 126; P > 0.05) submissions. Among frequent social media users, YouTube (86%), Facebook (76%), and Twitter (41%) were most commonly used; 12% post content or links frequently to these sites. The most common perceived roles of social media in health care were for inter-professional communication (67%) or as a simple information repository (59%); online patient interaction was endorsed by 14% of urologists. Fewer than 19% had read published guidelines for online patient interaction, and ≤64% were unaware of their existence. In all, 94.6% agreed that physicians need to exercise caution personal social media posting, although 57% felt that medical regulatory bodies should 'stay out of [their] personal social media activities', especially those in practice <10 years (P = 0.001). In all, 56% agreed that social media integration in medical practice will be 'impossible' due to privacy and boundary issues; 73% felt that online interaction with patients would become unavoidable in the future, especially those in practice >20 years (P = 0.02). CONCLUSION: Practicing urologists engage infrequently in social media activities, and are almost universal in avoiding social media for professional use. Most feel that social media is best kept to exchanges between colleagues. Emerging data suggest an increasing involvement is likely in the continuing professional development space.
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 | 0,002 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».