Ignite Creation Date:
2025-12-24 @ 11:04 PM
Ignite Modification Date:
2025-12-25 @ 8:35 PM
Study NCT ID:
NCT06988969
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-09-16
First Post:
2025-05-17
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Predicting Vaccine Hesitancy Using Machine Learning
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000072758', 'term': 'Vaccination Refusal'}, {'id': 'D000088823', 'term': 'Vaccination Hesitancy'}], 'ancestors': [{'id': 'D016312', 'term': 'Treatment Refusal'}, {'id': 'D000074822', 'term': 'Treatment Adherence and Compliance'}, {'id': 'D015438', 'term': 'Health Behavior'}, {'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 600}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-07-02', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-14', 'studyFirstSubmitDate': '2025-05-17', 'studyFirstSubmitQcDate': '2025-05-23', 'lastUpdatePostDateStruct': {'date': '2025-09-16', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-05-25', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-11-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Parental Vaccine Hesitancy Status', 'timeFrame': 'Day 1 (Parents will be sent the questionnaire and asked to respond promptly.)', 'description': "The Vaccine Hesitancy Scale is a measurement tool designed to assess individuals' vaccine hesitancy or opposition. It consists of 21 items across 4 subscales and uses a 5-point Likert scale format. The four subscales of the scale are: Vaccine Benefit and Protective Value, Vaccine Opposition, Solutions for Avoiding Vaccination, and Justification of Vaccine Hesitancy. Higher scores on the scale indicate greater levels of vaccine hesitancy or opposition."}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Children', 'Vaccines', 'Hesitancy', 'Machine learning', 'Immunization', 'Parent'], 'conditions': ['Vaccine Refusal', 'Vaccine Hesitancy', 'Machine Learning', 'Children']}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.\n\nThis 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\nParents who meet the following criteria will be included in the study:\n\n* Have proficiency in understanding and reading the Turkish language,\n* 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,\n* Are 18 years of age or older,\n* Have children aged 0-4 years,\n* Consent to participate in the study,\n* Have children without any absolute contraindications to vaccination.\n\nExclusion Criteria:\n\n* Parents of high-risk infants (such as those with a history of systemic illness, preterm labor, anomalies, etc.),\n* Parents who are unwilling to participate in the study will be excluded.'}, 'identificationModule': {'nctId': 'NCT06988969', 'briefTitle': 'Predicting Vaccine Hesitancy Using Machine Learning', 'organization': {'class': 'OTHER', 'fullName': 'University of Yalova'}, 'officialTitle': 'Factors Influencing Vaccine Hesitancy Among Parents of Children Aged 0-48 Months: A Machine Learning Prediction', 'orgStudyIdInfo': {'id': 'Emel Avcin MLT'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Parents of children aged 0-48 months', 'description': 'Parents who meet the following criteria will be included in the study: those who have proficiency in understanding and reading the Turkish language; are 18 years of age or older; have children aged 0-4 years; 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; consent to participate in the study; and whose children do not have any absolute contraindications for vaccination.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '77200', 'city': 'Yalova', 'country': 'Turkey (Türkiye)', 'facility': 'Yalova University', 'geoPoint': {'lat': 40.65501, 'lon': 29.27693}}], 'overallOfficials': [{'name': 'EMEL AVÇİN, Assist Prof', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of Yalova'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Yalova', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Ass Prof', 'investigatorFullName': 'Emel AVÇİN', 'investigatorAffiliation': 'University of Yalova'}}}}