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Harnessing R Software and Artificial Intelligence for Inductive Qualitative Insights on Missed Opportunities for Vaccination

DOI: 10.4236/jdaip.2026.142013, PP. 254-277

Keywords: Missed Opportunities for Vaccination (MOV), Caregivers, Health Workers, Qualitative Analysis, Inductive, COM-B Model, AI and R

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Abstract:

Background: Missed opportunities for vaccination (MOV) hinder full immunization coverage, with evidence suggesting that an MOV results from multiple stakeholder factors. Understanding these determinants and finding practical solutions is crucial for improving the Expanded Program on Immunization. This study investigates caregivers’ and health workers’ perspectives on MOV reasons and reduction strategies in Cameroon. Methods: We performed qualitative analysis using data from a cross-sectional study. Open-ended responses underwent R-based processing (tokenization, stop-word removal, topic modeling) and AI-assisted inductive analysis to generate and validate themes through manual review. We proposed an R tutorial to conduct such analyses. Results: Caregivers cited vaccine stockouts, service interruptions, lengthy waits, and competing priorities, resulting in frustration or missed doses. Health workers identified training gaps, heavy workloads, documentation problems, and logistical issues. Mapping findings to the COM-B (Capability, Opportunity, Motivation-Behavior) model revealed knowledge gaps, opportunity constraints, and motivational challenges. Recommended solutions included continuous vaccine availability, improved provider-caregiver communication, appointment reminder systems, enhanced staff training, and better supervision. AI-assisted analysis enabled systematic theme identification and validation. Conclusion: Reducing MOV requires integrated approaches addressing capability, opportunity, and motivation among caregivers and providers. Combining inductive analysis with AI-driven analysis offers actionable insights for implementing interventions to strengthen immunization systems.

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