Use of Apps to Promote Childhood Vaccination: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Vaccination is a critical step in reducing child mortality; however, vaccination rates have declined in many countries in recent years. This decrease has been associated with an increase in the outbreak of vaccine-preventable diseases. The potential for leveraging mobile platforms to promote vaccination coverage has been investigated in the development of numerous mobile apps. Although many are available for public use, there is little robust evaluation of these apps. OBJECTIVE: This systematic review aimed to assess the effectiveness of apps supporting childhood vaccinations in improving vaccination uptake, knowledge, and decision making as well as the usability and user perceptions of these apps. METHODS: PubMed, Excerpta Medica Database (EMBASE), Web of Science, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and Education Resources Information Center (ERIC) databases were systematically searched for studies published between 2008 and 2019 that evaluated childhood vaccination apps. Two authors screened and selected studies according to the inclusion and exclusion criteria. Data were extracted and analyzed, and the studies were assessed for risk of bias. RESULTS: A total of 28 studies evaluating 25 apps met the inclusion criteria and were included in this analysis. Overall, 9 studies assessed vaccination uptake, of which 4 reported significant benefits (P<.001 or P=.03) of the implementation of the app. Similarly, 4 studies indicated a significant (P≤.054) impact on knowledge and on vaccination decision making. Patient perceptions, usability, and acceptability were generally positive. The quality of the included studies was found to be moderate to poor, with many aspects of the methodology being unclear. CONCLUSIONS: There is little evidence to support the use of childhood vaccination apps to improve vaccination uptake, knowledge, or decision making. Further research is required to understand the dichotomous effects of vaccination-related information provision and the evaluation of these apps in larger, more robust studies. The methodology of studies must be reported more comprehensively to accurately assess the effectiveness of childhood vaccination apps and the risk of bias of studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/16929.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".