An Educational Network for Surgical Education Supported by Gamification Elements: Protocol for a Randomized Controlled Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND Traditionally, medical students have learned surgical skills by observing a resident physician or surgeon who is performing the technique. Due to inconsistent practice opportunities in the clinical setting, a disparity of skill levels among students has been observed. In addition, the poor availability of faculty professors is a limiting factor in teaching and adequately preparing medical students for their clerkship years. With the ongoing COVID-19 pandemic, medical students do not have access to traditional suturing learning opportunities. Didactic courses are available on videoconferencing platforms; however, these courses do not include technical training. OBJECTIVE Our overarching goal is to evaluate the efficacy and usability of web-based peer-learning for advanced suturing techniques (ie, running subcuticular sutures). We will use the Gamified Educational Network (GEN), a newly developed web-based learning tool. We will assess students’ ability to identify and perform the correct technique. We will also assess the students’ satisfaction with regard to GEN. METHODS We will conduct a prospective randomized controlled trial with blinding of expert examiners. First-year medical students in the Faculty of Medicine of Université de Montréal will be randomized into four groups: (1) control, (2) self-learning, (3) peer-learning, and (4) peer-learning with expert feedback. Each arm will have 15 participants who will learn how to perform running subcuticular sutures through videos on GEN. For our primary outcome, the students’ ability to identify the correct technique will be evaluated before and after the intervention on GEN. The students will view eight videos and rate the surgical techniques using the Objective Structured Assessment of Technical Skills Global Rating Scale and the Subcuticular Suture Checklist as evaluation criteria. For our secondary outcomes, students will anonymously record themselves performing a running subcuticular suture and will be evaluated using the same scales. Then, a survey will be sent to assess the students’ acceptance of the intervention. RESULTS The study will be conducted in accordance with the Declaration of Helsinki and has been approved by our institutional review board (CERSES 20-068-D). No participants have been recruited yet. CONCLUSIONS Peer learning through GEN has the potential to overcome significant limitations related to the COVID-19 pandemic and the lack of availability of faculty professors. Further, a decrease of the anxiety related to traditional suturing classes can be expected. We aim to create an innovative and sustainable method of teaching surgical skills to improve the efficiency and quality of surgical training in medical faculties. In the context of the COVID-19 pandemic, the need for such tools is imperative. CLINICALTRIAL ClinicalTrials.gov NCT04425499; https://clinicaltrials.gov/ct2/show/NCT04425499 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/21273
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 | 0,026 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,002 |
| Méta-épidémiologie (sens large) | 0,010 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,070 | 0,010 |
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 ».