Children Immunization App (CIMA): A Non-randomized Controlled Trial Among Syrian Refugees in Zaatari Camp, Jordan
Bibliographic record
Abstract
Approximately 20 million children are not vaccinated, especially among refugees. There is a growing access to smartphones, among refugees, which can help in improving their vaccination. We assessed the impact of an app for the vaccination follow-up visit among refugees in Jordan. We developed an app and tested it through a non-randomized trial at the Zaatari refugees camp in Jordan. The study was conducted during March - December 2019 at three vaccination clinics inside the camp. The study included two study groups (intervention and control groups) for refugees living at the camp. The intervention group included parents who own an Android smartphone and have one newborn that require between one and four first vaccination doses and they accepted to participate in the study, during their regular visit to the vaccination clinics. The control group was for the usual care. We compared both study groups for returning back to one follow-up visit, using Kaplan-Meier survival analysis. We recruited 936 babies (n = 471; 50.3% in the intervention group, both study groups were similar at baseline). The majority of mothers were literate (94.2%) with a median age of 24. The majority of the babies had a vaccination card (n = 878, 94%). One quarter (26%) of mother-babies pairs of the intervention group came back within one week (versus 22% for control group); When it comes to lost-follow-up, 22% and 28% did not have a history of returning back (intervention and control groups respectively, p = 0.06) (Relative risk reduction: 19%). The Kaplan-Meier Survival Analysis showed a statistically significant progressive reduction in the duration of coming back late for the follow-up vaccine visit. We tested a vaccination app for the first time, in a refugee population setting. The app can be used as a reminder for parents to come back on time for their children's vaccine follow-up visits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".