A Design-Led Theory of Change for a Mobile Game App (Go Nisha Go) for Adolescent Girls in India: Multimix Methodology Study
Notice bibliographique
Résumé
BACKGROUND: India has one of the largest adolescent populations in the world. Yet adolescents, particularly adolescent girls, have limited access to correct sexual and reproductive health information and services. The context in which adolescent girls live is one of gender inequity where they contend with early marriage and early pregnancy and have few opportunities for quality education and labor force participation. The digital revolution has expanded the penetration of mobile phones across India, increasingly being used by adolescent girls. Health interventions are also moving onto digital platforms. Evidence has shown that applications of game elements and game-based learning can be powerful tools in behavior change and health interventions. This provides a unique opportunity, particularly for the private sector, to reach and empower adolescent girls directly with information, products, and services in a private and fun manner. OBJECTIVE: The objective of this paper is to describe how a design-led Theory of Change (ToC) was formulated for a mobile game app that is not only underpinned by theories of various behavior change models but also identifies variables and triggers for in-game behavioral intentions that can be tracked and measured within the game and validated through a rigorous post-gameplay outcome evaluation. METHODS: We describe the use of a multimix methodology to formulate a ToC informing behavioral frameworks and co-design approaches in our proof-of-concept product development journey. This process created a statement of hypothesis and "pathways to impact" with a continuous, cumulative, and iterative design process that included key stakeholders in the production of a smartphone app. With theoretical underpinnings of social behavior and modeling frameworks, systematic research, and other creative methods, we developed a design-led ToC pathway that can delineate complex and multidisciplinary outputs for measuring impact. RESULTS: The statement of hypothesis that emerged posits that "If girls virtually experience the outcomes of choices that they make for their avatar in the mobile game, then they can make informed decisions that direct the course of their own life." Four learning pathways (DISCOVER, PLAY, DECIDE, and ACT) are scaffolded on 3 pillars of evidence, engagement, and evaluation to support the ToC-led framework. It informs decision-making and life outcomes through game-based objectives and in-game triggers that offer direct access to information, products, and services. CONCLUSIONS: This approach of using a multimix methodology for identifying varied and multidisciplinary pathways to change is of particular interest to measuring the impact of innovations, especially digital products, that do not necessarily conform with traditional behavioral change models or standard co-design approaches. We also explain the benefits of using iterative and cumulative inputs to integrate ongoing user feedback, while identifying pathways to various impacts, and not limiting it to only the design and development phase.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».