A Design-Led Theory of Change for a Mobile Game App (Go Nisha Go) for Adolescent Girls in India: Multimix Methodology Study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".