Children Immunization App (CImA) Among Syrian Refugees in Zaatari Camp, Jordan: Protocol for a Cluster Randomized Controlled Pilot Trial Intervention Study
Bibliographic record
Abstract
BACKGROUND: There are up to 19.4 million children who are still unvaccinated and face unnecessary deaths, especially among refugees. However, growing access to smartphones, among refugees, can be a leading factor to improve vaccination rates. OBJECTIVE: This study aims to determine whether a smartphone app can improve the vaccination uptake among refugees and determine the app's effectiveness in improving the documentation of vaccination records. METHODS: We developed and planned to test an app through a cluster randomized trial that will be carried out at the Zaatari refugee camp in Jordan. The study will be open to all parents who carry Android smartphones, have at least one child, and agree to participate in the study. The parents will be recruited to the study by trained volunteers at the vaccination sites around the Zaatari camp. Inclusion criteria will be the following: having at least one child of 0 to 5 years, being a local resident of the camp, and having an Android smartphone. RESULTS: The intervention includes an app that will allow storing Jordanian vaccination records, per child, on the parents' smartphones in Arabic and English (in an interchangeable fashion). Every record will have a set of automated reminders before the appointment of each child. The app will summarize immunization records in form of due, taken, or overdue appointments, labeled in orange, green, and red, respectively. Baseline will include the collection of our primary and secondary outcomes that are needed for the pre and postdata measurements. This includes social demographic data, any previous vaccination history, and electronic health literacy. Participants, in both study arms, will be monitored for their follow-up visits to the clinic for vaccination doses. For the study outcome measures, we will measure any differences in the uptake of vaccinations. The secondary outcome is to analyze the effect of the children immunization app on visits for follow-up doses. CONCLUSIONS: Owing to the limited evidence of effective interventions for childhood vaccination among refugees, research in this area is greatly needed. The project will have a significant impact on the health of refugees and the public health system. In Jordan and the Middle East, the vaccination level is low. Given the influx of refugees from the area, it is crucial to ensure a high vaccination level among the children. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13557.
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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 | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".