From trends to theories in urban mental health

Open Access
Authors
Supervisors
Cosupervisors
Award date 22-05-2025
Number of pages 296
Organisations
  • Faculty of Social and Behavioural Sciences (FMG) - Psychology Research Institute (PsyRes)
  • Faculty of Social and Behavioural Sciences (FMG)
Abstract
This thesis addresses how psychological research can move “from trends to theories” in two distinct ways across two parts. Part I focuses on transforming data trends into robust phenomena within urban mental health research. Although over half of humanity resides in cities, findings remain inconsistent regarding how urban life influences well-being, social satisfaction, and mental health. To address this gap, a continuous measure of urbanicity is introduced that avoids arbitrary boundaries and reveals that UK city living is generally linked to lower and more variable well-being. Further analyses indicate that psychological difficulties accumulate disproportionately among those already struggling, while the last chapter of Part I shows country-specific associations between mental health and urbanicity in Norway, the UK, and New Zealand.

Part II turns to systems approaches to shift psychological theories from fleeting trends to lasting scientific contributions, in line with Meehl’s famed observation that ''Theories in soft areas of psychology… tend to neither be refuted nor corroborated, but instead simply fade away as people lose interest.'' As a step in this direction, the thesis first discusses the widely used Ising model and clarifies its dual role: both as a statistical likelihood function and as a system model representing real-world phenomena. Then it broadens this perspective by introducing a comprehensive framework of probabilistic network models, offering practical guidance for simulating, testing, and refining network-based theories. Finally, the thesis advocates a foresighted approach that fosters cumulative theory development, concluding with directions for bridging urban phenomena with the proposed framework network models.
Document type PhD thesis
Language English
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