How can we best understand mental health in the UK?
Critically and empirically investigating the measurement, and socio-demographic and geographical predictors of UK mental health.

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

Abstract

Mental Health is an issue of increasingly accepted importance in the UK, both in policy and in the public sphere. It becomes clear under scrutiny, however, that any attempt to characterise and prioritise “at risk” groups risks making conclusions entirely sensitive to the definition of mental health chosen by the researcher. This thesis examines the definition and distribution of the mental health conditions which have been the backdrop for this increase in awareness. This involves combining innovative quantitative methodologies in order to develop a comprehensive understanding of UK mental health from 2009-2016. The thesis is divided into two halves.
The first half addresses the definition of mental health. Mental health measurement can be crudely categorised into theories which characterise mental health via absence of negative elements and presence of positive elements. Unpacking what is truly captured by the most widely used population screening metrics for each is undertaken using Exploratory Structural Equation Modelling (ESEM). Solutions for both metrics are presented and demonstrated to be both substantively and empirically superior to traditional summed interpretation.
The second half of the thesis incorporates these newly suggested interpretations into an investigation of the distribution of mental health across the UK using advanced multilevel modelling techniques. Firstly, a demonstration of what could be known given the summed interpretation of the metrics is given. This demonstrates the differences between the geographical and demographic determinants of the metrics. Finally, the constructs developed from the ESEM analysis become responses in a longitudinal investigation of chronicity in mental health.
This presents the first comprehensive overview of UK mental health incorporating both ESEM and Multilevel Modelling. It represents clear evidence of the synergy of the two methodologies. Moreover, the empirical findings of the thesis are relevant and important for central and local governments in developing geographically-sensitive mental health policy.
Date of Award23 Jan 2019
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorKelvyn Jones (Supervisor) & George B Leckie (Supervisor)

Keywords

  • Mental Health
  • Geography
  • Measurement
  • Psychology
  • Wellbeing
  • Multilevel Modelling
  • Structural Equation Modelling
  • Advanced Quantitative Methods
  • Epidemiology

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