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Record W4386745256 · doi:10.1186/s12978-023-01665-1

Study protocol for an individually randomized control trial for India's first roleplay-based mobile game for reproductive health for adolescent girls

2023· article· en· W4386745256 on OpenAlex
Ananya Saha, Anvita Dixit, Lalita Shankar, Madhusudana Battala, Nizamuddin Khan, Niranjan Saggurti, Kavita Ayyagari, Aparna Raj, Susan Howard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReproductive Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of Ottawa
FundersUnited States Agency for International Development
KeywordsRandomized controlled trialReproductive medicinemHealthReproductive healthBaseline (sea)Focus groupAgency (philosophy)MedicinePsychologyMedical educationApplied psychologyPsychological interventionPopulationNursingMarketingPregnancy

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.041
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.201
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.530
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it