Effectiveness of a Mobile Phone Application to Increase Access to Sexual and Reproductive Health Information, Goods, and Services Among University Students in Uganda: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background: University students are one of the most vulnerable groups to sexual reproductive health [SRH] threats like sexually transmitted infections [STIs], unwanted pregnancies, and unsafe abortions and often have limited access to SRH information, goods, and services. This study assessed the effectiveness of using a mobile phone application (APP) to increase access to SRH information, goods, and services among university students in Uganda.Methods: Using data from a double-blinded randomized controlled trial, participants were randomly assigned to both the intervention (APP) and control (standard of care) arms. We executed descriptive analyses for baseline demographic characteristics by intervention, difference in difference (DID), and quantile regression analyses for both primary and secondary outcomes.Results: The median age of participants was 21 years of age, and the majority were female (over 60%), unemployed (over 85%) and Christian (90%). Over 50% were resident in off-campus hostels and in a relationship. Between baseline and end-line, there was a significant increase in SRH knowledge score (DID=2, P<0.001), contraceptive use (DID=6.6%, P<0.05), HIV Voluntary testing and counselling (DID=17.2%, P<0.05), STI diagnosis and treatment (DID=12.9%, P<0.05), and condom use at last sex (DID=4%,P<0.05). There was a significant 0.98 unit increase in knowledge score (adjusted coefficient= 0.98, P<0.001), a significant 1.6-fold increase in odds of contraceptive use (adjusted coefficient= 1.6, P<0.05), a significant 3.5-fold increase in HIV VCT (adjusted coefficient=3.5, P<0.05), and a significant 2-fold increase in odds of STI testing and treatment (adjusted coefficient =1.9, P<0.001) after adjusting for demographic characteristics.Conclusion: A mobile phone application increased sexual and reproductive health information (knowledge score), access to goods (contraceptives), and services (HIV voluntary testing and counseling and sexually transmitted infection diagnosis and management) among sexually active university students in Uganda. Further technical development, including the refinement of youth-friendly attributes, as well as further research into potential economic impact and paths to sustainability, is needed before the app is deployed to the general youth population in Uganda and other low-income settings. Trial registration: MUREC1/7 No. 07/05–18. Registered on June 29, 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".