C-CASE 2023: Promoting Excellence in Surgical Education01. Near-peer tutoring: an effective adjunct for virtual anatomy learning02. Surgical Exploration and Discovery: perspectives on student-led debriefs in undergraduate surgical education03. How are emotions of medical residents and faculty in general surgery affected by new assessment mandates implemented in medical education?04. Equity, diversity and inclusion in medical residency: a Web-based analysis of Canadian PGME efforts05. The use of virtual patient case simulations in surgical postgraduate teaching innovations06. Outcomes of the Canadian Orthopaedic Surgery Medical Education Course07. Educational effectiveness of social media as a continuing professional development intervention among practising surgeons: a systematic review08. Factors influencing medical students’ decision to pursue a career in surgery in North America: a systematic review09. Improving operating room teamwork: Do current strategies offer actionable solutions?10. Affective influence on lumbar laminectomy performance on a virtual reality spine simulator11. Exploring the shifting landscape in the match outcomes of women applying to surgical residency programs in Canada: Have we achieved equity?12. Exploring residents’ emotion-regulation strategies and their effectiveness in team-based surgical simulations13. Impact of an educational workshop for medical student surgical clerkship rotation preparation14. McGill Surgical Exploration and Discovery: impact on medical students’ perception and skills in surgery15. Does BlackBox Explorer Technology improve operative teaching for surgical residents?16. The impact of a deceased donor organ procurement workshop on surgical transplant fellows’ confidence and competence17. Vascular anastomosis workshop improves general surgery residents’ confidence and competence18. A comparative analysis of video-based versus text-based learning resources for Foley catheter insertion and the implications for medical student procedural skills training: a pilot study19. Improving assessment and training in craniosynostosis: the role of interactive 3-D models and multimodal e-learning20. Anatomy of a strategic plan for education: driving innovation in McMaster’s Division of Cardiac Surgery21. Optimizing the learner’s role in feedback: development of a feedback-preparedness online application for medical students in the clinical setting22. Trainee response to artificial intelligent tutor instruction in surgical simulation23. Promoting diversity in surgery through UofC UpSurge24. Evaluating the benefit of a novel surgical robotic training workshop for general surgery and subspecialty trainees in Canada25. Undergraduate surgery learning objectives: illuminating discrepancies among Canadian medical schools26. Cerebral corticectomy in ex-vivo calf brain model: face and content validation27. Validating a virtual simulation tool for local and regional flap reconstruction in the head and neck28. The impact of a surgical boot camp and near-peer teaching on the acquisition of basic surgical skills in medical students transitioning to clerkship29. Evaluating the impact of the MLASE checklist on surgical training literature involving machine learning and virtual reality: a comparative analysis30. Exploring the impact of LearnENT’s social media as a powerful tool in OHNS medical education31. MiHolo, a holographic training tool for minimally invasive mitral valve repair surgery32. Evaluating the impact of medical students’ participation in telephone visits for patients with breast cancer: a pilot study33. The importance of understanding the personalities of surgeons when choosing a career: medical students’ perspectives34. A conversation on the current state of diversity, equity and inclusion in surgery35. Current state of female and BIPOC representation in Canadian medical education administration leadership36. What’s in a name? The development of trust and familiarity in dynamic interprofessional operating room teams37. Development and pilot of a plastic and reconstructive surgery case-based learning curriculum38. Women in surgery: a qualitative study of gendered experiences within everyday surgical life39. An interdisciplinary complex airway crisis resource management simulation training module for otolaryngology residents: a longitudinal experience40. Assessing the accuracy of responses by ChatGPT to questions regarding pediatric surgery
Notice bibliographique
Résumé
Background: The start of the COVID-19 pandemic caused a shift in medical education from the classroom to the virtual setting.This abrupt change led to an increase in stress among students.In response, McGill University medical students piloted an anatomy club, hosting virtual events to prepare fellow students for their examinations.Near-peer teaching is a form of education in which junior students are tutored by more experienced students.Methods: Review presentations and mock examinations were created using cadaveric images from textbooks, with a region-based approach.As anatomy examinations use in-person cadaveric models, these sessions focused on identifying pertinent anatomic landmarks for students to properly orient themselves using typical prosection views.Results: In the Fall 2021 semester, these sessions had an average attendance rate of 80% (162/202) and 81% (169/208) among the classes of 2024 and 2025, respectively.A feedback survey was sent to all attendees to determine student satisfaction (29% response rate).Most students felt strongly that our sessions helped them prepare for examinations (4.84/5.00),improved their overall understanding of anatomy (4.61/5.00)and reduced examination-related stress (4.55/5.00).These mean Likert scores and attendance rates are greater than those for similar student-led initiatives, as reported in the literature.High-quality resources provided by the university library and collaboration from faculty members were key to the success of this initiative.Conclusion: Nearpeer anatomy tutoring sessions were very popular among McGill University medical students (80% turnout).We encourage students from all medical schools to implement similar programs, given the high satisfaction and low cost with the use of faculty-supplied resources.
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 | 0,010 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,157 | 0,038 |
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 ».