Personalized Text Messages and Automated Calls for Improving Vaccine Coverage Among Children in Pakistan: Protocol for a Community-Based Cluster Randomized Clinical Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND A major reason for poor childhood vaccine coverage in developing countries is the lack of awareness among parents and caregivers regarding the need for immunization and the importance of completing the entire series of vaccines. Short message service (SMS)–based interventions have been quite effective in different programs such as smoking cessation, treatment adherence, health care scheduled appointment attendance, antenatal care attendance, and compliance to immunization. However, there are limited data from low- and middle-income countries on the role of SMS and automated call–based messages and interventions to improve routine immunization (RI) coverage. OBJECTIVE The primary objective of this study is to evaluate whether automated mobile phone–based personalized messages (SMS or automated call) can improve RI uptake at 6, 10, and 14 weeks of age per the expanded program immunization schedule, compared with a usual care control group. Secondary objectives include assessing the effects of different types of automated SMS text or calls on RI coverage at 20 weeks of age. METHODS This is a mixed methods study using a clustered randomized controlled trial with 4 intervention arms and 1 control arm, augmented by qualitative interviews for personalizing the message. The study is being conducted in Pakistan (an urban site in Karachi and a rural site Matiari). In Karachi, 250 administrative structures are taken as 1 cluster, whereas in Matiari, a catchment area of 4 Lady Health Workers is considered as 1 cluster. The intervention targets families to receive weekly 1-way or 2-way (interactive) personalized automated SMS or automated phone call messages regarding vaccination. Possible barriers to vaccination are assessed in each family at the time of inclusion to determine the type of personalized messages that should be sent to the family to increase the chance of a positive response. Finally, in-depth interviews using purposive sampling are conducted before and after the trial to determine the family’s vaccination experience and related factors. RESULTS All study participants for the cluster randomized trial were enrolled by January 14, 2019. Study exit interviews at 20-weeks follow-up visits will be completed by June 2019. CONCLUSIONS The results of this study will be useful to understand the respective effects of SMS text messages versus automated phone–based communication to improve RI coverage and timelines. Moreover, information regarding families’ perceptions of vaccination and the daily life challenges for timely visits to the vaccine clinic will be used for developing more complex interventions that use mobile phone messages and possibly other approaches to overcome barriers in the uptake of correct and timely immunization practices. CLINICALTRIAL ClinicalTrials.gov NCT03341195; https://clinicaltrials.gov/ct2/show/NCT03341195 (Archived by WebCite at http://www.webcitation.org/78EWA56Uo) INTERNATIONAL REGISTERED REPOR DERR1-10.2196/12851
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.011 |
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".