Digital Health Interventions to Improve Vaccination Rates and Awareness Among Immigrant Populations: Barriers, Facilitators, and Outcomes - A Scoping Review
Bibliographic record
Abstract
Background: Digital health interventions are suggested to improve vaccine coverage and awareness in the general population. However, given the scarcity of information within migrant populations, this scoping review aims to identify existing evidence on digital health interventions designed to improve vaccination and health outcomes among this social group. Methods: In this scoping review, we searched CENTRAL, PubMed, and Scopus for observational studies and randomized controlled trials (RCT). Two independent reviewers screened articles, performed data extraction and synthesis, and assessed bias risk using CovidenceⓇ. Bias was evaluated with the Cochrane RoB 2, Newcastle-Ottawa Scale (NOS), or JBI tool. We analyzed digital health interventions aiming to boost vaccination rates and awareness among immigrant populations, evaluating barriers and facilitators. The focus was on vaccines such as COVID 19, HPV, Hepatitis B, Influenza, childhood vaccines. Targeting immigrants, primarily from South and East Asia, the Middle East, and Hispanic/Latinx populations. The interventions of interest included digital appointment reminders, mobile applications, messaging platforms, and digital storytelling. Findings: Out of the 673 studies initially identified, 19 met the criteria for data extraction and synthesis. Published between 2012 and 2024, these included six quasi experimental studies, five cross-sectional studies, four randomized control trials, three qualitative studies and one survey. Research spanned several continents and countries such as North America, Germany, China, Jordan,Turkey and Uganda. The role of digital tools in increasing the vaccination rate must be reinforced, with particular emphasis on the personalized content of the message for recipients. The measurement tools influencing vaccination rates are not only diverse but also complex. They encompass a wide range of factors, from knowledge about immunization to emotions and vaccine intention, highlighting the multifaceted nature of the issue. Several factors interfere with vaccination rates (e.g., language barriers, costs, long wait times, scheduling difficulties, lack of transportation, and child support). Some confounders may impact the effectiveness and uptake of vaccination programs in undocumented immigrants from seeking vaccination services, such as socioeconomic status, education level, language barriers, cultural beliefs and practices, distrust in the healthcare system, legal status and fear of deportation. Interpretation: Digital health interventions show promise in enhancing vaccination awareness among migrant populations. Findings from this scoping review suggest that these interventions should be customized for specific populations, taking into account barriers, facilitators, and cultural beliefs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".