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Enregistrement W4409759326 · doi:10.2196/66289

A Serious Game (Health Unit in Focus) for Enhancing Undergraduate Education on Older Adults’ Health: Design and Validation Study

2025· article· en· W4409759326 sur OpenAlexvenueno aff
Yuri Gustavo de Sousa Barbalho, Calliandra Maria de Souza Silva, Carla Sílvia Fernandes, R Trombini, Pedro Paulo Tavares de Melo, Aline Farias de Oliveira, Alayne Larissa Martins Pereira, Alessandro de Oliveira Silva, Luciano Ramos de Lima, Marina Morato Stival, Diana Lúcia Moura Pinho, Silvana Schwerz Funghetto

Notice bibliographique

RevueJMIR Serious Games · 2025
Typearticle
Langueen
DomainePsychology
ThématiqueEducational Games and Gamification
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPreprintUnit (ring theory)Focus (optics)Serious gamePsychologyMedical educationComputer scienceGerontologyMedicineMultimediaMathematics educationWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Background: Population aging underlines the critical need to improve health professional training to adequately care for adults aged >60 years. Developing educational resources to support academics and professionals presents a valuable opportunity to enhance understanding of health conditions and improve clinical management. Serious games are designed to develop teaching, training, and learning skills. Their use in the educational setting is warranted, as they integrate digital aspects and gamification to create a playful experience for content acquisition. Deepening this theme in nursing education will improve assistance to the older adult population, leading to more qualified care based on gerontological practices and comprehensive health care for older adults. Objective: This study aims to develop and validate a serious game on older adult health for undergraduate nursing students. Methods: This quantitative and descriptive methodological study, conducted between February 2023 and December 2023 at a public university in the Federal District of Brazil, involved the active participation of 27 undergraduate nursing students in their eighth to tenth semesters. The game, Health Unit in Focus (HUF), was developed and validated with their input. It features 75 clinical cases distributed across 3 themes: pharmacology, metabolic syndrome, and semiology. Of the 40 students initially enrolled, 27 completed the study. The app was validated using the System Usability Scale and student feedback, and the results were reported following the Game-Based Intervention Reporting Guidelines (GAMING). Results: The participants had a mean age of 22.67 (SD 1.44) years, were mostly female (20/27, 74%), and were in their eighth semester (26/27, 96%). The game received an average System Usability Scale score of 85.75 (median 86.57), classified as excellent, as all evaluated items scored >75. Participants considered the game easy to use; accessible; practical; and rich in well-founded, useful content. This high usability score, coupled with the overwhelmingly positive feedback from the students, instills confidence in the game's effectiveness. Furthermore, 100% (27/27) of students agreed that learning through games is effective and expressed interest in incorporating more interactive games into their training. The serious game HUF showed good usability, as its overall score was "excellent," with its highest score in the odd-numbered items that addressed the positive aspects identified in the analysis. Conclusions: The serious game HUF is not just a valid and reliable tool for training nursing students but also an engaging and interactive approach to learning. Its ability to captivate and involve students in the learning process is a testament to its potential to revolutionize nursing education. It is essential that the development of new methodological resources, such as serious games, be based on scientific evidence to guarantee greater reliability and success in achieving their established objectives.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,929
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,388
Écart entre enseignants0,361 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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