Virtual Mentorship in the Age of Social Media
Notice bibliographique
Résumé
To the Editor: The use of social media for education and networking flourished during the COVID-19 pandemic. As international medical graduates (IMGs), it has positively impacted our journeys in pathology. This letter discusses our experience with collaborating via social media and highlights specific issues associated with such endeavors. #PathTwitter is a thriving X (formerly known as Twitter) community of pathologists that has facilitated many successful collaborations. For instance, we met virtually during a project centered around an immunostain ubiquitous in pathology laboratories. Interestingly, while our mentor was a professor in the United States, the 2 of us were located in Singapore and Canada, respectively. Additionally, the 3 of us had never met in person and knew each other only through interactions on X. Fortunately, that did not hamper our collaboration, even though we were working from 3 time zones. Our experience has been similar collaborating with other medical students, residents, or faculty via social media. In contrast to conventional mentor–mentee relationships that involve periodic in-person meetings, virtual mentorship offers distinct advantages. For IMGs, it provides opportunities to obtain advice from trainees or attending physicians about residency or fellowship applications while remaining in their home country. For physicians in practice, it facilitates access to expertise outside of one’s institution for research projects or challenging cases. Such an arrangement also cuts the cost of travel and accommodations, reducing the financial burden on young professionals looking to build their careers. Although sustaining a mentor–mentee relationship over social media platforms may appear convenient, practical considerations, such as time zone differences and technological compatibility between mentor and mentee, must be taken into account when organizing meetings. In such instances, meetings are planned with intention, as opposed to spontaneous drop-ins that in-person settings allow. Eventually, relationships built over social media platforms may yearn for the warmth of face-to-face interactions. Sometimes, the mentor and mentee meet in person after a virtual interaction. On X, this phenomenon has given rise to the popular acronym “MOTTIRL,” which stands for “met-on-Twitter-then-in-real-life.” It captures the ineffable feeling of warmth when the physical interaction between mentor and mentee materializes after working with each other virtually for a while—for months or even years. All in all, virtual mentorship may pose unique challenges, but our personal experiences have shown it can be a great equalizer, allowing young professionals to access expertise remotely and experts to work with talent in otherwise hard-to-reach areas. Lavisha S. Punjabi, MBBS Senior resident, Anatomical Pathology, Singapore General Hospital, Singapore; email: [email protected]; X (formerly Twitter): @punjbiopsy; ORCID: https://orcid.org/0000-0003-0479-1690 Abhimanyu Tushir, MD Resident physician, Anatomical Pathology and Clinical Pathology, Temple University Hospital, Philadelphia, Pennsylvania
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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».