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

克服残疾人和长期健康问题及其无薪钙人士进入劳动力市场的障碍

• 批准号: 2531721
• 金额: --
• 依托单位: University of Stirling
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2531721
• 关键词: Overcoming; Barriers; Labour; Market; Entry

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.

博士将使用现有的三个高质量的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护理数据有现有的联系，所有调查都有相当大的覆盖范围，涵盖在苏格兰经历了卫生与卫生--丙型肝炎的个人。每项调查都有其独特的优势，可以将调查结果与工作受限人群的短期、中期和长期就业动态进行三角划分。数据集是：英国劳动力调查(UK LFS)采用轮换小组设计，每个家庭在一年内接受五次采访。详细问题包括就业特点、限制工作的残疾和护理。它将支持对就业的短期动态和个人的卫生与卫生状况的分析，特别是在比较就业类型和质量方面的优势。了解社会(UKHLS)每年跟踪一大批家庭样本，收集一系列关于就业和健康状况的信息。这一数据集允许按卫生署/卫生署健康状况比较中长期就业动态苏格兰健康调查(SHES)(链接到行政健康记录)提供有关个人健康状况的详细信息，并结合医院入院行政记录的纵向信息。就业状况只能在一个时间点上测量，但这个数据集将允许分析一段时间内的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、面板数据方法和多水平模型。大规模的数据将使人们能够分析围绕着强迫症和强迫症经历的复杂的社会异质性。这项研究的方法论贡献将是在评估不同的就业情况和定义时，评估替代的衡量选择。