Development of a Serious Game to Simulate Neonatal Intensive Care Unit Experiences: Collaborative Quasi-Experimental Study
Notice bibliographique
Résumé
BACKGROUND: Opportunities for neonatal intensive care unit (NICU) training are limited for medical and nursing students due to patient safety concerns and the complexities of neonatal care. In addition, the COVID-19 pandemic significantly disrupted clinical training opportunities, further underscoring the need for alternative educational tools that can provide immersive and practical learning experiences. Serious games have garnered attention as potential tools for medical education; however, few are designed to simulate the complete NICU environment and its unique challenges. OBJECTIVE: To address the educational gaps in neonatal care training, we aimed to develop and evaluate a serious game that provides a comprehensive NICU simulation experience for students and the general public. METHODS: The game was developed over 14 months by a collaborative team that included a neonatologist, 4 medical students, and 1 art student, with a total cost of US $10,000. Initially created in TyranoBuilder (STRIKEWORKS), the game was later redeveloped in Unity with Naninovel to support multilingual functionality. Structured as a 6-chapter visual novel, the game follows a high school student observing the NICU during a hospital internship. Scenario-based decision-making and interactive dialogues guide the player through both the clinical and emotional aspects of neonatal care. After completing the game, players were invited to participate in an optional web-based survey that assessed demographic information, gameplay quality, and educational value using Likert scales. Descriptive and inferential statistics were used for data analysis. RESULTS: The game, titled First Steps in the NICU, was released for iOS, Android, and Steam. As of May 2025, it has been downloaded 2799 times (2260 on iOS and 539 on Android). A total of 160 survey responses were collected, with 46.3% of respondents identifying as health care professionals or students. The majority of participants were female (114/160, 71.3%) and aged 20-29 years (59/160, 36.9%). Mean scores for length, difficulty, and gameplay were 3.05 (SD 0.62), 2.49 (SD 0.76), and 3.65 (SD 0.77), respectively, indicating a well-balanced design. The educational usefulness of the game received high ratings: empathy with the story (4.24), usefulness for knowledge acquisition (4.16), and effectiveness of serious games as a learning tool (4.37). No significant differences in evaluations were found between health care professionals and students and the general public, suggesting broad accessibility and appeal. CONCLUSIONS: We developed a low-cost serious game that simulates NICU experiences through collaboration between a neonatologist and students. The game received positive feedback and demonstrated educational value for a diverse audience. Positioned as formative research, this study highlights the potential of serious games to supplement neonatal care education. Future updates will incorporate user feedback, leading to improvements in gameplay and expanded content.
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 | 0,011 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».