Study protocol: using a mobile phone-based application to increase awareness and uptake of sexual and reproductive health services among the youth in Uganda. A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Several cost-effective programs are being implemented around the world that use mobile technology to improve Sexual and Reproductive Health (SRH) uptake and awareness among youth. Mobile phone applications are a viable and effective means of increasing access to SRH services and tools in low and middle-income countries. This paper presents a protocol for a pilot study of a novel program, a mobile phone-based sexual and reproductive health services awareness and delivery application with the objective of increasing the demand for SRH services amongst the youth in Uganda. METHODS: The study employs rigorous evaluation methods to ascertain the impact of the mobile application. We propose a randomized control trial study to determine the causal effect of the mobile phone app in creating awareness and increasing uptake of sexual and reproductive health services in Uganda. The main outcome of the impact evaluation is the percentage change in the SRH services and tools uptake, SRH knowledge and sexual behavior. We will also conduct a model-based incremental cost-effectiveness analysis (CEA) and budget impact analysis (BIA). The main outcomes of the economic evaluation will be the average cost per app user, cost per app service and tool provided. We will also test the in-app advertising model as a way to generate revenue to sustain the program subsidies and related costs. DISCUSSION: The study seeks to establish the proof of concept of using a mobile application to increase create awareness and increase uptake of SRH tools and services among youth in Uganda. The study results will lead to the development of a demand-driven, culturally-relevant, and easy-to-use mobile app to enhance the uptake of SRH services among the youth in Uganda and globally. TRIAL REGISTRATION: MUREC1/7 No. 07/05-18 . Registered 29th June 2018.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
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".