The Impact of Digital Technologies on HPV Vaccination Uptake and Intention Among Adolescents, Young Adults and their Parents: a Systematic Review
Bibliographic record
Abstract
Background: Human papillomavirus (HPV) is a preventable sexually transmitted infection and the major risk factor for cervical cancer. The global prevalence of HPV infection is high, especially in low- and mid- income countries (LMIC). HPV immunization has been shown to reduce high-grade cervical cancer lesions and cancer incidence. Despite global HPV vaccination programs initiated in 2006, uptake remains insufficient. Digital media offers a promising avenue to address vaccine hesitancy and improve vaccination rates among adolescents and their parents. Objective: To evaluate the effectiveness of digital media interventions on HPV vaccination rates and intentions. Methods: We undertook a comprehensive search for PubMed, Scopus, Web of Science, Cochrane databases, from inception until May 2024. The review included Randomized Controlled Trials (RCTs) and observational studies focusing on adolescents and young adults (9-26 years) and their parents, and it investigated the impact of digital interventions on vaccination rates and vaccination intention. The quality assessments were assessed using the Cochrane Risk of Bias 2 tool and an adapted Newcastle-Ottawa Scale for RCTs and observational studies respectively, while data extraction focused on study design, demographics, intervention types, and outcomes. Results: From 2350 records, 23 studies (19 RCTs and 4 observational studies) met the inclusion criteria. Digital interventions, including text messages, mobile apps, video content, and web-based platforms, improved HPV vaccination intentions. Narrative-based videos were particularly effective in promoting parents and adolescents’ immunization, especially when the vaccine was offered immediately after the intervention. Nevertheless, barriers such as challenges with access, misconceptions, and socioeconomic factors often hinder translating of intentions into actual uptake. Conclusion: Digital interventions showed great potential in increasing short-term HPV vaccination intentions. Future research should focus on long-term engagements to enhance the effectiveness of digital interventions in promoting HPV immunization uptake. KEYWORDS: Human papillomavirus viruses; Vaccination, Digital Technology, Young adults, Adolescents, Parents;
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".