Creating a Healthcare Entrepreneurship Teaching Program for Medical Students
Notice bibliographique
Résumé
Introduction: The future of healthcare hinges on effective adoption of innovative solutions. Arguably, physicians are ideally positioned to propel clinical innovation given their firsthand experience with healthcare challenges; however, physicians often lack the necessary skills in innovation development and implementation methodology. The gap is partly a result of the paucity of exposure to innovation and entrepreneurship concepts within medical education and postgraduate training. To address this gap, the University of Toronto’s distributed medical education campus in Mississauga created a novel teaching initiative designed to impart themes of healthcare entrepreneurship to early stage medical learners. Methods: To inform the design of the program, the authors conducted a series of semi-structured interviews with key stakeholders, including physician entrepreneurs, innovation leaders, curriculum specialists and medical students. Using thematic analysis, key recommendations were extracted regarding learning objectives, approach to program delivery, and anticipated outcomes. A well-established entrepreneurial teaching model, the MaRS Entrepreneurship Framework, was adapted to frame the curricular content to the needs of medical learners. The resulting educational product consisted of six sessions, taught by subject matter experts, which outlined a methodological approach to the development of a medical start-up as a means of launching an innovation. Results: From November 2019 to May 2020, six sessions were held with a total of 37 unique attendees. The authors found that the series generated interest in entrepreneurship among medical students while fostering an appreciation for the basic principles of entrepreneurship. Conclusion: The next stage involves further program evaluation to guide the next iteration of the program. Potential avenues for growth include delivering the series virtually to support greater student accessibility. Future considerations include incorporating entrepreneurship into core undergraduate medical curricula and creating a dual degree program in medicine and entrepreneurship that cater to students with a deep interested in the field of healthcare entrepreneurship. Disclosure: The authors have no conflict of interest to declare. As all data were completely anonymized and no patients were involved, this was not reviewed by an ethics board.
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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 | 0,003 |
| 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 | 0,000 |
| 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 ».