Study protocol for an individually randomized control trial for India's first roleplay-based mobile game for reproductive health for adolescent girls
Bibliographic record
Abstract
BACKGROUND: Go Nisha Go™ (GNG), is a mobile game combining behavioural science, human-centric design, game-based learning, and interactive storytelling. The model uses a direct-to-consumer (DTC) approach to deliver information, products, services, interactive learning, and agency-building experiences directly to girls. The game's five episodes focus on issues of menstrual health management, fertility awareness, consent, contraception, and negotiation for delay of marriage and career. The game's effectiveness on indicators linked to these issues will be measured using an encouragement design in a randomized controlled trial (RCT). METHODS: A two-arm RCT will be conducted in three cities in India: Patna, Jaipur, and Delhi-NCR. The first arm is the treatment (encouragement) arm (n = 975) where the participants will be encouraged to download and play the game, and the second arm (n = 975) where the participants will not receive any nudges/encouragement to play the game. They may or may not have access to the game. After the baseline recruitment, participants will be randomly assigned to these two arms across the three locations. Participants of the treatment/encouragement arm will receive continuous support as part of the encouragement design to adopt, install the game from the Google Play Store at no cost, and play all levels on their Android devices. The encouragement activity will continue for ten weeks, during which participants will receive creative messages via weekly phone calls and WhatsApp messages. We will conduct the follow-up survey with all the participants (n = 1950) from the baseline survey after ten weeks of exposure. We will conduct 60 in-depth qualitative interviews (20 at each location) with a sub-sample of the participants from the encouragement arm to augment the quantitative surveys. DISCUSSION: Following pre-testing of survey tools for feasibility of methodologies, we will recruit participants, randomize, collect baseline data, execute the encouragement design, and conduct the follow-up survey with eligible adolescents as written in the study protocol. Our study will add insights for the implementation of an encouragement design in RCTs with adolescent girls in the spectrum of game-based learning on sexual and reproductive health in India. Our study will provide evidence to support the outcome evaluation of the digital mobile game app, GNG. To our knowledge this is the first ever outcome evaluation study for a game-based application, and this study is expected to facilitate scalability of a direct-to-consumer approach to improve adolescent sexual and reproductive health outcomes in India. TRIAL REGISTRATION NUMBER: ctri.nic.in: CTRI/2023/03/050447.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.137 | 0.016 |
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".