Bridging Cultures to Defeat COVID-19: An Innovative Virtual Exchange Program in Global Medical Education
Notice bibliographique
Résumé
ABSTRACT The problem and opportunity There is a critical and growing need to train globally focused, culturally fluent clinicians and scientists who can collaboratively defeat current and future public health threats across international boundaries. In parallel, academic conferences bring together thousands of diverse international healthcare professionals every year, yet their potential to provide the crucial professional development training necessary to advance internationalized medicine is often underutilized. The solution We developed and now first report an innovative healthcare education program that used an academic conference as the framework around which to build a structured, non-incidental virtual exchange (VE) for training globally and culturally proficient healthcare professionals. Herein we further describe the program’s design and content, successes and challenges, and lessons learned. Program Overview Using a smartphone based social-networking and conference management app with available translation capabilities, pre- and post-graduate trainees prepared and participated in poster presentations, seminars, and workshops to learn current research and best-practices in COVID-19 medicine, while engaging with their international peers in networking and professional-development exercises. The 2-week intensive program included daily synchronous interactive seminars on various topics in COVID-19 medicine, international team-based asynchronous activities such as preparing, presenting, and constructively critiquing research posters at virtual poster sessions, and expert-led wellness and cultural-competence workshops. Participants received initial training in the norms of intercultural communication, syllabus content and expectations, incentives, icebreaker activities, and program technology. They learned then-current COVID-19 medical research, therapies, and best practices, as well as professional "soft skills" including leadership, team building, scientific/clinical presentation, verbal/written communication skills, and intercultural competence. The program vastly expanded participants’ international professional networks to enhance their mentorship and career development opportunities. Conclusions Participants reported receiving substantial benefits from the program, with many reporting immediate translation of lessons learned toward improving healthcare education or practice in their home communities. TEASER Widespread innovative use of academic conferences as vehicles for structured non-incidental virtual exchange, professional development, and global medical education could improve healthcare education, capacity, and outcomes worldwide. KEY MESSAGES We developed and piloted a novel virtual exchange modality to connect international health science trainees and practitioners for unique collaborative training opportunities. Our "nested virtual exchange" concept employed an academic conference framework as the vehicle for providing structured cross-national didactics and professional development activities. This model aims to train a globally proficient next generation of clinicians and scientists who are optimally equipped to tackle current and future global health concerns. Our highly scalable, flexible, and efficient model can be adapted to any scientific or medical topic or focus, and is suitable for in-person, virtual, or hybrid approaches. It is especially suitable for student/trainee-led initiatives. Widespread adoption of this innovative training approach by universities, professional societies, and conference planners worldwide would equip many more healthcare providers and scientists with the knowledge and skills required to tackle public health challenges across international boundaries, thus improving global health outcomes. We hope that other universities, conference planners, and especially students and trainees will accept the baton to develop and launch similar programs to expand internationalized science and medicine worldwide.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».