Environmental scan of mobile apps for promoting sexual and reproductive health of adolescents in low- and middle-income countries
Bibliographic record
Abstract
Background: Adolescence is a period of emotional, mental, and physical change. To increase health seeking behaviors, reduce risky sexual behavior, and improve sexual and reproductive health (SRH) knowledge, adolescents require support and access to SRH services. Providing evidence-informed SRH knowledge to adolescents in low- and middle-income countries (LMICs) can be a challenge as they face unique barriers such as lack of confidentiality, fear of refusal, and stigma from cultural norms. Increasing availability of mobile apps necessitates a comprehensive evaluation of the quality and classification of these SRH mobile applications so that accurate and evidence-based information is reaching its users. Failure to provide SRH services can have damaging effects throughout their development. Objective: Provide an overview of current adolescent SRH (ASRH) mobile applications targeting adolescents in LMICs by evaluating their quality and classifying their characteristics. Methods: 21 search terms related to ASRH mobile apps was developed. These terms were searched in the Apple IOS store and Google Play stores. Inclusion and exclusion criteria were used to screen these apps. Resulting apps were assessed using the Mobile App Rating Scale (MARS) tool. Data extracted was used to rank order each app and identify any gaps in quality. Results: Search strategy yielded 2,165 mobile apps. Of these, only 8 were assessed using the MARS tool. Functionality subdomain scored highest at 4.6, while Information scored lowest at 2.5. None of the assessed apps contained information on the MARS items: Evidence base and Goals. Too Shy to Ask had the highest individual app mean score of 4.1, while e-SRHR scored lowest at 2.3. Conclusions: The goal of this study is to classify and rate the quality of mobile apps designed to promote ASRH behaviors and knowledge in LMICs. Numerous apps were reviewed and all of them failed to provide evidence-based and goal oriented SRH information. Strengths include ease of use, navigation, and gestural designs. Weaknesses include evidence base, goals, willingness to pay, customization, and interactivity. These findings can be potentially used to guide future app development and educate decision makers responsible for policy changes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".