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Record W4415706836 · doi:10.2196/77022

Effectiveness of an Education Toolkit Delivered by Soap Operas Among Communities Living in Extreme Poverty in Improving Vaccination Confidence in the Philippines: Protocol for a Cluster Randomized Controlled Trial

2025· article· en· W4415706836 on OpenAlexaffvenue
Quanfang Dong, Z. Zhang, Sharon Pang, Kevin E. Thorpe, Melinda Kelly, Victoria Haldane, Lincoln Lau, Xiaolin Wei

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsProtocol (science)Randomized controlled trialSOAPCluster (spacecraft)PovertyCluster randomised controlled trialVaccination

Abstract

fetched live from OpenAlex

BACKGROUND: Measles and polio pose significant public health challenges globally, particularly in low-resource settings such as the Philippines, where vaccine coverage falls short of the World Health Organization's (WHO's) targets, with hard-to-reach populations contributing to the "last mile." This research addresses the "last mile" challenge in routine immunization efforts by bridging the vaccination gap in marginalized populations. OBJECTIVE: We describe the implementation of a cluster randomized controlled trial to evaluate the impact of an education toolkit aimed at improving confidence in measles and polio vaccines among communities living in extreme poverty in the Philippines. METHODS: Developed with local stakeholders, our intervention consists of a 10-minute video and vaccination reminders delivered by health trainers. It is embedded within the Soap Opera Trial, a large cluster randomized controlled trial conducted by the International Care Ministries that evaluates a 15-episode soap opera series combining drama with aspirational messages on hope, self-worth, and education, aimed at improving participants' knowledge and practices in health, hygiene, nutrition, and livelihood. A total of 180 communities with 5400 participants will be randomly assigned to the intervention and control arms. By leveraging an existing community-based education program on health and livelihood run by our local partner, the proposed intervention will be delivered to participants in the intervention arm of the existing program, while those in the control arm will receive standard participatory adult learning sessions on health education. The primary outcome is the first-dose measles-containing vaccine coverage among participants' children aged 1 year. Secondary outcomes include the 2-dose measles-containing vaccine coverage among children aged 2 to 6 years, polio vaccination coverage among children aged 1 year, and participants' knowledge of measles and polio vaccines. The absolute differences in these outcomes between the intervention and control arms will be estimated using generalized estimating equations while adjusting for baseline levels and covariates. In addition, we will conduct a process evaluation. RESULTS: Between January 31 and February 29, 2024, we recruited 66.9% (3613/5400) of the participants for the trial. Data collection is ongoing at the time of manuscript submission. CONCLUSIONS: Findings from this trial will provide critical insights into effective strategies for enhancing vaccine confidence and uptake in marginalized populations. By leveraging community-based approaches and local partnerships, this study aims to improve public health responses to vaccine-preventable diseases and contribute to global efforts to eradicate measles and polio. Furthermore, the findings will inform scalable interventions that can be adapted to similar contexts, potentially reducing health disparities and advancing global health equity. TRIAL REGISTRATION: ClinicalTrials.gov NCT06218368; https://clinicaltrials.gov/study/NCT06218368. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77022.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.067
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.032
Meta-epidemiology (narrow)0.0070.004
Meta-epidemiology (broad)0.0120.007
Bibliometrics0.0040.004
Science and technology studies0.0050.004
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0670.009

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.

Opus teacher head0.125
GPT teacher head0.507
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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