Gamification and Game-Based Strategies for Dermatology Education: Narrative Review
Notice bibliographique
Résumé
BACKGROUND: Game-based approaches, or gamification, are popular learning strategies in medical education for health care providers and patients alike. Gamification has taken the form of serious educational games and simulations to enable learners to rehearse skills and knowledge in a safe environment. Dermatology learners in particular may benefit from gamification methods, given the visual and procedural nature of the field. OBJECTIVE: This narrative review surveys current applications of gamification within general medical training, in the education of dermatology students, and in dermatology patient outreach. METHODS: A literature search was performed using PubMed, Google Scholar, and ResearchGate to access and review relevant medical education- and dermatology-related gamification studies published in peer-reviewed journals. Two independent researchers with education and experience in dermatology screened publications to select studies featuring a diversity of gamification approaches and study subjects for in-depth examination. RESULTS: A total of 6 general medical education-related and 7 dermatology-specific gamification studies were selected. Gamification generally increased motivation and engagement, improved reinforcement of learning objectives, and contributed to more enjoyable and positive educational experiences compared to traditional modes of instruction. Enhancing examination scores, building confidence, and developing stronger team dynamics were additional benefits for medical trainees. Despite the abundance of gamification studies in general medical education, comparatively few instances were specific to dermatology learning, although large organizations such as the American Academy of Dermatology have begun to implement these strategies nationally. Gamification may also a provide promising alternative means of diversifying patient education and outreach methods, especially for self-identification of malignant melanoma. CONCLUSIONS: Serious games and simulations in general medical education have successfully increased learner motivation, enjoyment, and performance. In limited preliminary studies, gamified approaches to dermatology-specific medical education enhanced diagnostic accuracy and interest in the field. Game-based interventions in patient-focused educational pilot studies surrounding melanoma detection demonstrated similar efficacy and knowledge benefits. However, small study participant numbers and large variability in outcome measures may indicate decreased generalizability of findings regarding the current impact of gamification approaches, and further investigation in this area is warranted. Additionally, some relevant studies may have been omitted by the simplified literature search strategy of this narrative review. This could be expanded upon in a secondary systematic review of gamified educational platforms.
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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».