Overcoming Barriers to Labour Market Entry Amongst People with Disabilities and Long-term Health Conditions and their Unpaid Ca

博士将通过使用三个现有的高质量大型社会调查数据集来解决研究问题。其中一项调查与行政卫生和社会保健数据有联系，所有调查都大量覆盖了苏格兰经历DH/DH-C的个人。每项调查都有其独特的优势，可以对调查结果进行三角测量，涵盖工作受限人员的短期、中期和长期就业动态。数据集是：英国劳动力调查（UK LFS）采用旋转面板设计，每个家庭在一年内接受五次采访。包括关于就业特点、限制工作的残疾和照顾的详细问题。它将支持就业和个人的DH/DH-C情况的短期动态分析，在比较就业的类型和质量方面具有特别的优势。理解社会（UKHLS）每年跟踪大量家庭样本，收集有关就业和健康状况的一系列信息。苏格兰健康调查（SHeS）（与行政健康记录相关联）提供了关于个人健康状况的详细信息，并结合了来自住院行政记录的纵向信息。就业状况仅在一个时间点测量，但该数据集将允许分析随着时间的推移健康轨迹及其与就业结果的联系。在分析数据集时，博士生将使用一系列统计方法，包括序列分析，逻辑回归，面板数据方法和多层次建模。大规模的数据将允许分析围绕DH和DH-C经验的复杂社会异质性。研究方法的贡献将是评估替代测量选项时，评估异质就业情况和DH/DH-C的定义。

The PhD will address the research questions by using three existing high-quality large social survey datasets. One of the surveys has an existing linkage to administrative health and social care data, and all have substantial coverage of individuals experiencing DH/DH-C in Scotland. Each survey has its own particular strengths, allowing triangulation of findings with coverage of short, medium and long run employment dynamics for people with work-limiting conditions. The datasets are: The UK Labour Force Survey (UK LFS) has a rotating panel design, with each household interviewed five times over a one-year period. Detailed questions on employment characteristics, work-limiting disabilities and caring are included. It will support the analysis of short-run dynamics in employment and DH/DH-C circumstances of individuals, with particular strengths in comparing the type and quality of employment. Understanding Society (UKHLS) follows a large sample of households in annual waves, collecting a range of information about employment and health status. This dataset allows the comparison of medium to long-run dynamics of employment by DH/DH-C statusThe Scottish Health Survey (SHeS) (linked to administrative health records) provides detailed information on individuals' health status, combined with longitudinal information from administrative records of hospital admissions. Employment status is only measured at one time point, but this dataset will allow the analysis of health trajectories over time and their links to employment outcomes.In analysing the datasets, the PhD student will make use of a range of statistical methods including sequence analysis, logistic regression, panel data methods and multilevel modelling. The large scale of the data will allow analysis of the complex social heterogeneities that surround DH and DH-C experiences. A methodological contribution of the research will be to evaluate alternative measurement options when assessing heterogeneous employment circumstances and definitions of DH/DH-C.