Thesis Title:
King’s Algorithm for Acceptance Likelihood Identification (KAALI): Training a Machine Learning Model for Identifying Job Acceptance Likelihood for Individuals with Mental Health Conditions
Abstract:
Individuals with mental illness (MI) experience unjust treatment in the labour market; contributing to, and facilitating symptoms of, their disease. Tackling this trend calls for a detailed understanding of mechanisms influencing hiring decisions and career trajectories, translating these findings into fruitful interventions for ensuring optimal, fair allocation of talent, and developments of methods for preventing wasted applications. Suffers of MI in all severities are not only able to work and strive in competitive employment, but explicitly want to do so. (Mental) health and employment are heavily interrelated. Especially for individuals with MI, employment facilitates quality of life, illness management and recovery. Employment services have been established as an integral part of early intervention programmes (EIP) for MI, with the aim of employment regain and/or maintenance. Implementing such services in EIP ensures higher employment rates than programmes without. Playing a centre role in the age of digital recruitment is the use of Artificial Intelligence (AI) and Machine Learning (ML). This tool, when fully developed, will be able to limit time and increase the chances of successful job seeking for people with MI, and therefore actively play a part in early intervention, as well as management of their condition. This will not only allow millions of patients to alleviate suffering but save the UK government substantial amounts of money.
Social Media:
https://www.linkedin.com/in/maximinlange/
First Supervisor:
Dr Ricardo Twumasi
Pathway:
Pathway 2: Life Course, Psychology & Health (LCPH)
Cohort:
2022-23
Publications:
Lange, M., & Twumasi, R. (2022). Anxious People, Please Apply! No Evidence for Decreased Perceptions of Employability in Individuals with Mental and Physical Illness Compared to Healthy Controls. North American Journal of Psychology, 24(2), 319-336.