Security education as a preventive safeguarding intervention for older persons in Nigeria : a narrative review

Abstract

PURPOSE : This narrative review aims to examine security education as a preventive safeguarding intervention for older persons in Nigeria. Drawing on social learning theory (SLT) and adult education principles, it argues that community-based protective learning can reduce harm, build protective agency and complement formal adult protection systems in resource-constrained contexts. DESIGN/METHODOLOGY/APPROACH : A narrative review was conducted, with literature from Scopus, Web of Science and Google Scholar, supplemented by grey literature from international development organisations. Sources addressed older persons’ security vulnerabilities, adult learning as a protective mechanism or safeguarding policy. SLT, extended by adult education principles, provides the analytical structure. FINDINGS : Three themes emerge. Firstly, older persons in Nigeria face structural risks that are rooted in institutional neglect and exclusion from protective decision-making, conditions that reactive safeguarding cannot address alone. Secondly, SLT provides a sound framework for preventive education, with its emphasis on observational learning, self-efficacy and social reinforcement, which fits Nigerian community contexts. Thirdly, security education generates protective outcomes across economic, social and civic domains, complementing formal protection systems and reducing exposure to financial exploitation, physical abuse, cybercrime, social isolation and climate risk through early prevention. ORIGINALITY/VALUE : To the best of the authors’ knowledge, this study offers one of the first conceptual examinations of security education as a preventive safeguarding strategy for older persons in Nigeria. It advances programme design principles, proposes integrating protective learning into policy and contributes to adult protection scholarship by establishing education-based prevention as a necessary complement to formal systems in resource-constrained contexts.