Text Message Reminders and Unconditional Monetary Incentives to Improve Measles Vaccination in Western Kenya: Study Protocol for the Mobile and Scalable Innovations for Measles Immunization Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Globally, 21 million children do not receive the measles vaccine each year. With high levels of mobile phone access and ownership, opportunities exist to leverage mobile health technologies to generate demand for immunization. OBJECTIVE: The aim of the Mobile and Scalable Innovations for Measles Immunization trial is to determine if text message (short message service, SMS) reminders, either with or without mobile phone-based incentives, can improve measles immunization coverage and timeliness in rural western Kenya. METHODS: This is a 3-arm, parallel, randomized controlled trial (RCT). Using simple randomization, caregivers in Siaya County, Kenya, will be randomized and evenly allocated to 1 of 3 study arms: (1) control, (2) SMS reminders only, and (3) SMS reminders plus a 150 Kenyan Shilling (KES) incentive. Participants assigned to the SMS group will be sent SMS reminders 3 days before and on the day before the measles immunization visit scheduled for when the child is 9 months of age. Participants in the incentive arm will, in addition to SMS reminders as above, be sent an unconditional 150 KES mobile-money incentive to their mobile phone 3 days before the child becomes 9 months of age. Children will be followed up to the age of 12 months to assess the primary outcome, a measles vaccination by 10 months of age. Log-binomial regressions will be used to calculate relative risks. RESULTS: Enrollment was completed in March 2017. We enrolled 537 caregivers and their infants into the following groups: control (n=179), SMS reminders only (n=179), and SMS reminders plus 150 KES (n=179). Results will be made publicly available in 2020. CONCLUSIONS: Few RCTs have examined the effect of text message reminders to improve measles immunization coverage. This is the first study to assess the effect of SMS reminders with and without unconditionally provided mobile-money incentives to improve measles immunization coverage. TRIAL REGISTRATION: ClinicalTrials.gov NCT02904642; https://clinicaltrials.gov/ct2/show/NCT02904642 (Archived by WebCite® at http://www.webcitation.org/78r7AzD2X). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/13221.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.012 |
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".