AbstractEvidence has shown that mental health varies within and across individuals and, importantly, over time. The current study aimed to identify trajectories of mental health over time and to analyse what predisposed individuals to exhibit these trajectories. Data from first five waves of the Understanding Society database, a sample of over 100,000 participants which was representative of the wider UK population, was used in order to conduct this analysis.
Ye’s (2009) model was deemed an appropriate dimensional representation and was found to be stable over time and to display concurrent validity with a range of associated covariates. Four trajectories, characterised by slope and intercept were extracted from the data and these represented stable periods of poor and good psychological distress, with steadily increasing or decreasing levels respectively. The stable group of low levels of psychological distress was labelled as the reference group. These trajectories had a wide range of biological social and psychological covariates regressed upon them. The analysis showed that a wide range of biological, social and psychological variables affected individual’s trajectories over time, with social variables such as income and job satisfaction having the largest affect on class membership. Personality characteristics such as neuroticism was also to have a strong association with individuals exhibiting persistently elevated psychological distress. Generally, biological characteristics had a smaller affect on class membership with the majority of ethnicities displaying no statistically significant relationship with class membership.
This research has utility as it demonstrates how individuals may exhibit similar levels of psychological distress at a given time period but may have vastly different trajectories in how they arrived at these points. This research demonstrated how through analysing longitudinal trajectories, mental health practitioners can develop a wider perspective of how psychological distress can predispose individuals to poorer outcomes and could be used to inform treatment options accordingly.
|Date of Award||Sept 2020|
|Supervisor||Gary Adamson (Supervisor) & Jamie Murphy (Supervisor)|
- Mental health
- Structural equation modelling