Psychological injury: A quantitative assessment of natural justice and the optimum management of psychological factors in compensations systems

Abstract

This thesis documents the creation of psychosocial support program and interdisciplinary clinics for people with insured injuries. Applying machine learning and artificial intelligence, insights are derived from eight single arm studies, including recovery pathways, feigning spectrum behaviour, and the impact of interventions for different insured injuries. A comprehensive narrative review shows the evolution of language from an instrument for cooperation, the means of incorporation, trauma, and the recent development of Generative Artificial Intelligence as language incarnate. Psychological injuries are elucidated. There is a review of literature showing how stakeholder interactions can impact recovery from injury and the need for a trauma informed care approach. The predictive value of verbal and non-verbal expressions of psychological distress on recovery are demonstrated through the application of the Manchester Colour Wheel to a cohort of 1098 injured workers. Machine learning models to compare recovery from work related shoulder injury and motor crash related whiplash, demonstrates the diverse factors in recovery from insured injury. Machine learning models were used to identify the significant psychosocial factors important to the vexing and costly problem of clinical non-attendance. Cut scores for simulation were determined for some common psychometric measures. Large Language Models were used to derive insights from more than 7472 injured workers using a new approach called "persona generation". So called "thinking" large language models generated recovery personas in 711 motor accident injured people. Time series analysis was used to show the locus of natural justice is not with laws per se but at the case manager or business unit level within compensation systems. Detailed recommendations were made for applying trauma informed care and artificial intelligence to maximise natural justice and improve the recovery journeys of people with insured injuries.