Knowledge Transfer Megaannum Analytics of Impacts on the Effectiveness of Youth Rehabilitation Programmes, and Influences on a Young Persons Engagement within their Intervention: A Thematic Analysis of Youth Offender Practitioners Experiences

The evaluation of youth rehabilitation programmes has traditionally relied on localized, short-term qualitative assessments that fail to capture longitudinal, cross-population dynamics. This paper introduces the concept of Knowledge Transfer Megaannum Analytics (KTMA), a framework designed to synthesize vast, historically deep datasets of practitioner experiences regarding youth offender engagement. By integrating large language models (LLMs) with hierarchical Bayesian knowledge transfer mechanisms, we automate and scale the inductive thematic analysis of qualitative practitioner interviews. This approach addresses the persistent bottleneck of manual coding while preserving the interpretive nuance required in social sciences.