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Can Government Level Interventions Mitigate Childhood Adversity in South Africa?

  • Full or part time
  • Application Deadline
    Monday, March 30, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Childhood adversity in the form of parental loss, imprisonment, mental health problems; or exposure to domestic violence or child abuse is associated with long-term negative health outcomes, especially mental health. Most research utilises a scale score cut-off of four or more childhood adversities to test associations, making it difficult to disentangle the contributions of specific adversities and their interactions to health outcomes. In addition, the vast majority of research stems from high income countries using cross-sectional samples. There is thus an urgent need to investigate childhood adversity and its associations with mental health outcomes in adulthood in low-and middle-income contexts where adversity is highest. More importantly, it is essential to find ways to mitigate the long-term effects of childhood adversity.

This PhD proposal uses data from a three-wave cohort study in South Africa for which waves 1 and 2 were collected from 1800 adolescents aged 10-17 in 2010-11 and 2011-12. Wave 3 is funded by the ERC and will take place during the duration of this PhD 2020-2022. The successful candidate will use advanced quantitative techniques to examine childhood adversity risk profiles, their links with mental health in adulthood, and the effects of government grants, free schooling, school feeding schemes and receipt of child welfare/protection services on mental health outcomes.

The student will join the community of quantitative social scientists in the School of Social and Political Science and have access to the training provided through the Q-Step Centre, AQMEN as well as the Department of Psychology. The student will complete postgraduate courses on statistical modelling, longitudinal data analysis, Latent Class Analysis and quasi-experimental methods.

This PhD is set within an ongoing study funded by the ERC and with multiple collaborators in South Africa and the UK. The PhD student will have the opportunity to spend a considerable amount of time in the fieldwork site in South Africa as part of the data collection team in order to learn how to conduct large-scale complex quantitative data collection, understand the context in which the data is collected and in which families live and to experience knowledge exchange and dissemination to local stakeholders.

Funding Notes

Applicants must meet the following eligibility criteria
• A good first degree (at least 2:1), preferably with a social science component
• Demonstrate an interest in, and knowledge of childhood adversity or mental health
• Have a good grounding in quantitative social science methods. Any prior knowledge of longitudinal data analysis would be desirable.
• Demonstrate ability to work as part of a team
• Experience of communicating with people from diverse backgrounds

Students must meet ESRC eligibility criteria. ESRC eligibility information can be found here*:

Award Details:
The scholarship is available as a +3 or a 1+3 programme depending on prior research training. This will be assessed as part of the recruitment process. The programme will commence in September 2020. It includes:
• an annual maintenance grant at the RCUK rate
• fees at the standard Home rate
• students can also draw on a pooled Research Training Support Grant, usually up to a maximum of £750 per year

Applications will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by 3rd April 2020. Interviews will take place on 9th April.

All scholarship awards are subject to candidates successfully securing admission to a PhD programme within The University of Edinburgh Centre for Social Work. Successful scholarship applicants will be invited to apply for admission to the relevant PhD programme after they are selected for funding.

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