An innovative mobile app for educating teens and young adults about reproductive health and wellness including sexually transmitted infections, pregnancy and contraception
Bibliographic record
Abstract
Objective: The authors studied the use of a new mobile app technology platform to offer easy access to accurate, evidence-based comprehensive reproductive health information with links to resources for teens and young adults to enhance wellness and reduce the risks of HIV, other STIs, pregnancy, and complications such as infertility and pelvic inflammatory disease.Methods: The Healthy Teens Mobile App was designed, developed, tested, and implemented to deliver up-to-date sexual and reproductive health education based on science and written by experts. Initial testing was done for useability, readability and ease of navigation of the app.Results: Beta testing among teens, young adults, parents, health educators, and nurse clinicians (n = 32) revealed the app was valuable, engaging, and could be revised to improve flow and functionality. 60% strongly agreed and 40% agreed the app was valuable, with important information and resources on sexual health and wellbeing. 55% strongly agreed and 45% agreed the images improved the experience of using the app and the text size was adequate for readability. Other comments were utilized to enhance the app, add more images, and decrease the amount of white space on certain sections of the app. The app was released in June 2024 in both the Apple & Google App Stores.Conclusions: Utilizing a mobile app to delivery evidence-based sexual and reproductive health information for teens and young adults is practical and well accepted by teens, young adults, parents, health educators, and nurse clinicians. Plans are underway to disseminate the app widely. Future plans include translating the app into Spanish, French and other languages to promote global sexual and reproductive health and wellness and reduce the risks of HIV, other STIs, pregnancy, and complications such as infertility and pelvic inflammatory disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".