Clinical trial NCT06988969: Predicting Vaccine Hesitancy Using Machine Learning
Official title: Factors Influencing Vaccine Hesitancy Among Parents of Children Aged 0-48 Months: A Machine Learning Prediction
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Vaccine Refusal
Vaccine Hesitancy
Machine Learning
Children
Keywords: Children
Vaccines
Hesitancy
Machine learning
Immunization
Parent
Lead sponsor: University of Yalova
Sponsor class: OTHER
First posted: 2025-05-25
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: No sample size calculation was performed in this study. Between June 2025 and September 2025, all parents of children aged 0-4 years who met the inclusion criteria and could be reached with the questionnaires were included in the study. Snowball sampling method will be used to reach the parents. The study population consists of parents who have children aged 0-4 years, possess proficiency in understanding and reading the Turkish language, and can be accessed via social media platforms using technological devices.

Brief summary: In recent years, emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Virtual Reality (VR) have rapidly become integrated into daily life. The widespread use of these applications has led to the accumulation of vast amounts of data, giving rise to what is commonly referred to as "Big Data." Due to the sheer volume, manual processing and analysis of these large datasets are not feasible. Therefore, software tools and libraries-such as Python and R libraries-have been developed to perform these analyses efficiently and to generate predictions for the future by leveraging historical data through Machine Learning (ML) algorithms.

The primary goal of machine learning algorithms is to discover patterns within existing data and use these patterns to make accurate predictions on new data. The use of machine learning in the field of healthcare has gained significant momentum in recent years. However, a review of the literature reveals that research specifically addressing childhood vaccine hesitancy remains limited.

This study aims to identify the factors contributing to vaccine hesitancy among parents of children aged 0-48 months and to develop a predictive model using machine learning techniques based on these factors. Such a model could help anticipate the likelihood of vaccine refusal among parents and thereby support the development of targeted public health strategies for at-risk populations.


Eligibility criteria: Inclusion Criteria:

Parents who meet the following criteria will be included in the study:

* Have proficiency in understanding and reading the Turkish language,
* Are able to use technological devices such as mobile phones or computers to access social media platforms (Participants used social media platforms such as Instagram and WhatsApp to communicate) via the internet,
* Are 18 years of age or older,
* Have children aged 0-4 years,
* Consent to participate in the study,
* Have children without any absolute contraindications to vaccination.

Exclusion Criteria:

* Parents of high-risk infants (such as those with a history of systemic illness, preterm labor, anomalies, etc.),
* Parents who are unwilling to participate in the study will be excluded.

Primary outcomes:
measure: Parental Vaccine Hesitancy Status
timeFrame: Day 1 (Parents will be sent the questionnaire and asked to respond promptly.)
Secondary outcomes:
Other outcomes:
Locations:
city: Yalova
country: Turkey
facility: Yalova University
Study officials:
affiliation: Yalova Unıversıty
name: EMEL AVÇİN, Assist Prof
role: PRINCIPAL_INVESTIGATOR
References: