Vaccipack, A Mobile App to Promote Human Papillomavirus Vaccine Uptake Among Adolescents Aged 11 to 14 Years: Development and Usability Study
Bibliographic record
Abstract
Background More than 90% of human papillomavirus (HPV)-related cancers could be prevented by widespread uptake of the HPV vaccine, yet vaccine use in the United States falls short of public health goals. Objective The purpose of this study was to describe the development, acceptability, and intention to use the mobile app Vaccipack, which was designed to promote uptake and completion of the adolescent HPV vaccine series. Methods Development of the mobile health (mHealth) content was based on the integrated behavioral model (IBM). The technology acceptance model (TAM) was used to guide the app usability evaluation. App design utilized an iterative process involving providers and potential users who were parents and adolescents. App features include a vaccine-tracking function, a discussion forum, and stories with embedded messages to promote intention to vaccinate. Parents and adolescents completed surveys before and after introducing the app in a pediatric primary care setting with low HPV vaccination rates. Results Surveys were completed by 54 participants (20 adolescents aged 11 to 14 years and 34 parents). Notably, 75% (15/20) of adolescents and 88% (30/34) of parents intended to use the app in the next 2 weeks. Acceptability of the app was high among both groups: 88% (30/34) of parents and 75% (15/20) of adolescents indicated that Vaccipack was easy to use, and 82% (28/34) of parents and 85% (17/20) of adolescents perceived the app to be beneficial. Higher levels of app acceptability were found among parents with strong intentions to use the app (P=.09; 95% CI –2.15 to 0.15). Conclusions mHealth technology, such as Vaccipack, may be an acceptable and nimble platform for providing information to parents and adolescents and advancing the uptake of important vaccines.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".