Use of Apps to Promote Childhood Vaccination: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: The decline in the uptake of routine childhood vaccinations has resulted in outbreaks of vaccine-preventable diseases. Vaccination apps can be used as a tool to promote immunization through the provision of reminders, dissemination of information, peer support, and feedback. OBJECTIVE: The aim of this review is to systematically review the evidence on the use of apps to support childhood vaccination uptake, information storage, and record sharing. METHODS: We will identify relevant papers by searching the following electronic databases: PubMed, Embase by Ovid, Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, and Education Resources Information Center (ERIC). We will review the reference lists of those studies that we include to identify relevant additional papers not initially identified using our search strategy. In addition to the use of electronic databases, we will search for grey literature on the topic. The search strategy will include only terms relating to or describing the intervention, which is app use. As almost all titles and abstracts are in English, 100% of these will be reviewed, but retrieval will be confined to papers written in the English language. We will record the search outcome on a specifically designed record sheet. Two reviewers will select observational and intervention studies, appraise the quality of the studies, and extract the relevant data. All studies will involve the use of apps relating to child vaccinations. The primary outcome is the uptake of vaccinations. Secondary outcomes are as follows: (1) use of app for sharing of information and providing vaccination reminders and (2) use of app for storage of vaccination information; knowledge and decision making by parents regarding vaccination (ie, risks and benefits of vaccination); costs and cost-effectiveness of vaccination apps; use of the app and measures of usability (eg, usefulness, acceptability, and experiences of different users: parents and health care professionals); use of technical standards for development of the app; and adverse events (eg, data leaks and misinformation). We will exclude studies that do not study an app. We anticipate a limited scope for meta-analysis and will provide a narrative overview of findings and tabular summaries of extracted data. RESULTS: This project was funded by the Sir David Cooksey Fellowship in Healthcare Translation at the University of Oxford, Oxford, United Kingdom. We will submit the full systematic review for publication in the Journal of Medical Internet Research. CONCLUSIONS: This review will follow, where possible, the Cochrane Collaboration and the Centre for Review and Dissemination methodologies for conducting systematic reviews. We will report our findings based on guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The review results will be used to inform the development of a vaccination app. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16929.
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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 | 0.079 | 0.094 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.102 | 0.015 |
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".