Game Design, Effectiveness, and Implementation of Serious Games Promoting Aspects of Mental Health Literacy Among Children and Adolescents: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: The effects of traditional health-promoting and preventive interventions in mental health and mental health literacy are often attenuated by low adherence and user engagement. Gamified approaches such as serious games (SGs) may be useful to reach and engage youth for mental health prevention and promotion. OBJECTIVE: This study aims to systematically review the literature on SGs designed to promote aspects of mental health literacy among adolescents aged 10 to 14 years, focusing on game design characteristics and the evaluation of user engagement, as well as efficacy, effectiveness, and implementation-related factors. METHODS: We searched PubMed, Scopus, and PsycINFO for original studies, intervention development studies, and study protocols that described the development, characteristics, and evaluation of SG interventions promoting aspects of mental health literacy among adolescents aged 10 to 14 years. We included SGs developed for both universal and selected prevention. Using the co.LAB framework, which considers aspects of learning design, game mechanics, and game design, we coded the design elements of the SGs described in the studies. We coded the characteristics of the evaluation studies; indicators of efficacy, effectiveness, and user engagement; and factors potentially fostering or hindering the reach, efficacy and effectiveness, organizational adoption, implementation, and maintenance of the SGs. RESULTS: We retrieved 1454 records through database searches and other sources. Of these, 36 (2.48%) studies describing 17 distinct SGs were included in the review. Most of the SGs (14/17, 82%) were targeted to a universal population of youth, with learning objectives mainly focusing on how to obtain and maintain good mental health and on enhancing help-seeking efficacy. All SGs were single-player games, and many (7/17, 41%) were embedded within a wider pedagogical scenario. Diverse game mechanics and game elements (eg, minigames and quizzes) were used to foster user engagement. Most of the SGs (12/17, 71%) featured an overarching storyline resembling real-world scenarios, fictional scenarios, or a combination of both. The evaluation studies provided evidence for the short-term efficacy and effectiveness of SGs in improving aspects of mental health literacy as well as their feasibility. However, the evidence was mostly based on small samples, and user adherence was sometimes low. CONCLUSIONS: The results of this review may inform the future development and implementation of SGs for adolescents. Intervention co-design, the involvement of facilitators (eg, teachers), and the use of diverse game mechanics and customization to meet the needs of diverse users are examples of elements that may promote intervention success. Although there is promising evidence for the efficacy and effectiveness of SGs for promoting mental health literacy in youth, there is a need for more rigorously planned studies, including randomized controlled trials and real-world evaluations, that involve follow-up measures and the assessment of in-game performance alongside self-reports.
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 | 0,006 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,007 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».