ADRENALINE, a Learning Game to Improve Prescribing Skills in Undergraduate Medical Students: Descriptive Study
Notice bibliographique
Résumé
Background: Junior doctors often demonstrate insufficient prescribing skills, highlighting the need to enhance undergraduate medical training in this area. Serious games (SGs) have proven effective in teaching knowledge and skills across various medical specialties including surgery and emergency care. To our knowledge, no SG specifically dedicated to prescribing has been developed to date. Our objective was to develop a new educational program, based on a learning game designed to enhance medical students' competencies in safe and effective prescribing. Objective: This study aimed to describe ADRENALINE, a learning game designed to promote safe and effective prescribing, and to report feedback from sixth-year undergraduate medical students at our medical school after the first year of its implementation in the therapeutics curriculum. Methods: This study implemented an interactive educational program based on Kolb experiential learning theory to enhance safe and effective prescribing skills among sixth-year medical students. The program followed 3 phases: a preliminary in-person lecture introducing the SG ADRENALINE, autonomous gameplay, and a final debriefing lecture. ADRENALINE, accessible via university platform Moodle (Andrews Lyons) on multiple devices, was developed using MOSAIC software (Katia Quelennec), a software program created to develop evolutive SG based on real-life professional situations, and includes 20 realistic clinical scenarios of varying difficulty, requiring students to make therapeutic decisions and receive immediate feedback. Players advance through levels based on performance, with ongoing support from professors via feedback and a dedicated forum. The program was integrated into the therapeutic curriculum of Lille University, and participation was voluntary. All 598 sixth-year students were invited to access the game via email and to participate in pre- and postintervention surveys assessing usage patterns, satisfaction, and learning outcomes. Results: Between November 2023 and March 2024, 272 sixth-year students accessed the ADRENALINE program. Of these, 201/272 (73.9%) students completed at least one scenario and obtained scores ranging from 16.5 to 100 out of 100. Pretest survey responses (n=99 answers) indicated that 92/99 (93%) students identified as gamers and believed that SGs could be relevant for their medical education. Posttest survey responses (n=50 answers) reflected a high level of satisfaction among participants. Most students reported that ADRENALINE helps them apply academic knowledge in real-world context, feel more confident with prescribing and managing adverse drug reactions, improve their prescribing skills, and better prepare for the national Objective Structured Clinical Examination. Conclusions: We developed a learning game focused on medical prescribing, designed to be easily shared with other French-speaking medical schools. Although only 201/598 (33.6%) students engaged with this initial version, 85% (42/50) of the feedback received was positive, indicating strong student interest and supporting the educational value of a game-based approach to enhance prescribing skills among undergraduate medical students.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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; 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 ».