The growth prerogative: how does an objective of economic growth influence local planning policy?

This research aimed to explore the privileging of growth and its influence on planning in England. The research examined two contrasting case studies: Middlesbrough Borough Council and Cambridge City Council. The analysis of growth privileging is rooted within a constructionist ontology which argues that planning is about the way in which people construct value relative to the function of land. This perspective enables the research to position growth privileging as a social construction; a particular mental frame for understanding and analyzing place based challenges and an approach which has been increasingly absorbed by the UK planning community. Through interviews with a range of planning actors, the first part of the research examined the state of planning in the current political and economic context and the influence that a privileging of growth has on planning. The second part of the research investigated the merits and feasibility of the capabilities approach as an alternative mental frame for planning, an approach developed through the work of Amartya Sen and Martha Nussbaum. The research results disaggregate the concept of economic growth, based on the responses of interviewees and conclude that it is characterized by homogeneity. Growth is valued, not only because of its economic role, for example, supporting jobs and income but its potential in creating diversity, enriching culture and precipitating transformative change. Pursuing growth as an objective has a range of influences upon planning. In particular, it supports a utilitarian framework for decision-making which values spatial decisions on their ability to support aggregate economic growth. The research demonstrates the feasibility and merits of the capabilities approach as a means with which to better understand the relationship between planning and human flourishing. Based on this analysis, the research proposes that the capabilities approach can provide an alternative ‘mental frame’ for planning which privileges human flourishing as the primary objective or ‘final end’ instead of economic growth.

“Enjoy your baby” Internet-based CBT for mothers with babies: a feasibility randomised control trial

Background: Postnatal depression is a global health problem with lasting effects on the family. Government policy is focussed on early intervention and increasing access to psychological therapies. There is a growing evidence base for the use of computerised CBT packages and this study investigated the feasibility of a CBT-based self-help internet intervention for new mothers. Objective: To assess the ability to recruit mothers, deliver an internet course, obtain follow-up data and evaluate what mothers think of the course. Design: A feasibility randomised control design was used to compare a waiting list control group (delayed access= DA) to the Enjoy Your Baby course (immediate access= IA). Measures were administered at baseline and 8 week follow-up. Methods: Adverts were placed in the Metro freesheet, on charity web pages, on social media, posters were put up in the community, and leaflets were handed out at mother and baby groups. Participants had to be 18 years old or over with a child less than 18 months old. The IA arm was given access to the course straight away. After 8 weeks all participants were asked to recomplete the original measures and those in the IA arm also gave feedback on the course. Participants in the DA arm were given access after recompleting the questionnaires. Due to a lack of follow-up data a small discussion group was conducted. Intervention: The course contains 4 core modules including helping mothers understand why they feel the way they do and helping them build closeness to their babies. Additional modules, worksheets and homework tasks were available. The DA group were given a list of additional support resources and services, and encouraged to seek additional help if required. All participants received weekly automated emails for 12 weeks as they worked through the course. It was not possible to deliver individualised support. 34 Results: Despite using a number of recruitment strategies, recruitment was lower and slower than anticipated, and attrition was high. 41 women, primarily recruited via the internet, were randomised (IA n=21, DA n=20). No significant differences were observed between participants in either arm at baseline and no statistically significant differences were identified when the demographics and baseline measures of participants who logged-on to the course were compared to those who did not, or when participants who completed follow-up measures were compared to those who did not. Pre and post intervention scores on the EPDS approached statistical significance (P=.059, r=.444) favouring the intervention arm. The discussion group suggested strengths of the course and recommended areas for improvement, including making the course more mobile friendly. Conclusion: Internet interventions show promise; however it is difficult to recruit mothers, engagement is low and attrition high. A number of recommendations are made and a further pilot or an internal pilot of a larger substantive study should be conducted to confirm recruitment and retention. Trial ID: ISRCTN90927910.

Methods matter: computational modelling in public health policy and planning

This work is aimed at understanding and unifying information on epidemiological modelling methods and how those methods relate to public policy addressing human health, specifically in the context of infectious disease prevention, pandemic planning, and health behaviour change. This thesis employs multiple qualitative and quantitative methods, and presents as a manuscript of several individual, data-driven projects that are combined in a narrative arc. The first chapter introduces the scope and complexity of this interdisciplinary undertaking, describing several topical intersections of importance. The second chapter begins the presentation of original data, and describes in detail two exercises in computational epidemiological modelling pertinent to pandemic influenza planning and policy, and progresses in the next chapter to present additional original data on how the confidence of the public in modelling methodology may have an effect on their planned health behaviour change as recommended in public health policy. The thesis narrative continues in the final data-driven chapter to describe how health policymakers use modelling methods and scientific evidence to inform and construct health policies for the prevention of infectious diseases, and concludes with a narrative chapter that evaluates the breadth of this data and recommends strategies for the optimal use of modelling methodologies when informing public health policy in applied public health scenarios.