P166: The chief resident incubator - a virtual community of practice
Notice bibliographique
Résumé
Introduction: The Emergency Medicine Chief Resident Incubator is a year-long curriculum for chief residents that aims to provide participants with a virtual community of practice, formal administrative training, mentorship, and opportunities for scholarship. Methods: The Chief Resident Incubator was designed by Academic Life in Emergency Medicine (ALiEM; www.aliem.com ) a digital health professions education organization in 2015, following a needs assessment in emergency medicine. A 12-month curriculum was created using constructivist social learning theory, with specific learning objectives that reflected 11 key administrative or professional development domains deemed important to chief residents. The topics covered included interviewing skills, contract negotiations, leadership, coaching, branding, conflict resolution, and ended with a focus on wellness and career longevity. A Core Leadership Team and Virtual Mentors were recruited to lead each annual iteration of the curriculum. The Incubator was implemented as a virtual community of practice using Slack©, a messaging and digital communication platform. Ancillary technology such as Google Hangout on Air© and Mailchimp© were used to facilitate learner engagement with the curriculum. Three in person networking events were hosted at three large emergency medicine and education conferences with special medical education guests. Outcomes include chief resident participation rates, Slack© activity, Google Hangout© web analytics, newsletter email engagement, and scholarship. We also incorporated a hidden curriculum throughout the year with multiple online publications, competitions for guest grand round presentations, and incorporation of digital technologies in medical education. Results: A total of 584 chief residents have participated over the first 3 years of the Chief Resident Incubator; this includes chief residents from over 212 residency programs across North America. Over 27,000 messages have been shared on Slack© (median 214 per week). A total of 32 Google Hangouts© have occurred over the course of the inaugural Incubator including faculty mentorship from Dr. Rob Rogers, Dr. Dara Kass and Dr. Amal Mattu. A monthly newsletter was distributed to the participants with an opening rate of 59%. Scholarship included 26 published academic blog posts, 2 open access In-Training exam prepbooks, a senior level online curriculum with 9 published modules and 3 book club reviews. Conclusion: The Chief Resident Incubator is a virtual community of practice that provides longitudinal training and mentorship for chief residents. This Incubator framework may be used to design similar professional development curricula across various health professions using an online digital platform.
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 | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,012 |
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 ».