Refining and embedding the Intersectional "MAIHDA" approach to intersectionality in quantitative social science research.

在我们的社会中，在一系列不同的事情上存在着巨大的不平等。关于健康，我们知道，在英国，白色人一般比非白色人更健康，富人比穷人更健康。除了健康问题，其他地方也存在类似的不平等现象--失业率、收入、教育水平等等，但不太为人所知的是这些不平等现象是如何变化和相互作用的。例如，我们知道在健康方面存在种族差距，但这对男性和女性来说是一样的吗？有钱女人和穷女人是一样的吗？当我们把各种特征结合起来，产生越来越小的群体时，很明显，我们社会中的不平等是复杂的。这种复杂性很重要：重要的是，我们要知道谁的健康状况更差，这样我们才能把政策针对正确的人;重要的是，我们要知道谁被我们的教育系统辜负了，这样我们才能思考如何改变这个系统，使其更加公平。从根本上说，要改善社会公正，首先要了解社会目前辜负了哪些人。MAIHDA方法是一种统计模型，它允许我们这样做：它让我们看到哪些特征组合与优势相关，哪些与劣势相关。这一创新方法在2016年至2022年期间逐步发展，并已告知我们一系列健康结果中不平等的性质。然而，该方法仍处于起步阶段，这笔赠款将完善该方法，使其能够用于以前从未使用过的情况。这包括评估政府政策，看看哪些群体从特定政策中受益，哪些群体受益较少甚至受到伤害。该项目还将使我们能够考虑复杂的不平等如何随着时间的推移而变化，以及它们如何因地而异。我们将使用真实的数据集来测试我们的方法改进，这些数据集将作为未来如何使用这些方法的范例，与肥胖、新冠肺炎和环境污染物有关。我们还热衷于将该方法的使用范围扩大到健康不平等之外。因此，我们将与学术研究中心和非学术组织的学者和研究人员合作，在其他社会科学学科领域实施MAIHDA方法。这将包括对护理人员的分析，以及护理人员如何以不同的方式影响不同的人群。它将包括对美国水质的分析，以了解不同地方的不同人群如何受到劣质污染饮用水的影响。我们将考虑不同人群的教育不平等情况如何变化，看看哪些群体被教育部门辜负了。最后，这笔赠款的一个关键部分是培训。我们希望提高学术界和其他领域的研究人员的技能，以便能够在未来使用这种方法以及我们对它的改进。因此，我们将制作各种各样的在线培训材料，并运行在线和面对面的培训课程，针对既定的学者，博士生和非学术研究组织。这些材料将与其他利益攸关方一起编写，以确保它们满足这些组织和个人的需要。

There are big inequalities in our society, in a range of different things. Regarding health, we know that, in the UK, white people are generally more healthy than non-white people, and rich people are more healthy than poorer people. Beyond health, there are similar inequalities everywhere - in rates of unemployment, in income, in education levels, and so on.What is less well known is how these inequalities vary and interact. For instance, we know there is an ethnicity gap in health, but is that the same for men and women? Is it the same for rich women and poorer women? As we combine characteristics to produce smaller and smaller groups, it becomes clear that the inequalities in our society are complicated. And that complexity matters: it's important that we know who has worse health, so that we can target policies to the right people; it's important that we know who is being failed by our education system, so we can think about how the system could be changed to be more equitable. Fundamentally, in order to improve social justice, we need to first understand who is currently being let down by society. And that needs to be more than just a comparison of broad categories of people.The MAIHDA method is a statistical model that allows us to do this: it lets us to see which combinations of characteristics are associated with advantage, and which are associated with disadvantage. This innovative method has been incrementally developed between 2016 and 2022, and has already informed us about the nature of inequalities in a range of health outcomes.However, the method remains in its infancy, and this grant will refine the method to allow it to be used in situations that it has never been used before. This includes evaluating government policies to see which groups benefit from a particular policy, and which groups benefit less or even are harmed. The project will also allow us to consider how complex inequalities have changed over time, and how they vary from place to place. We will test our methodological refinements using real datasets which will act as exemplars in how these methods could be used going forward, relating to (among other things) obesity, covid-19, and environmental pollutants.We are also keen to expand the use of the method beyond health inequalities. As such, we will collaborate with academics and researchers in academic research centres and non-academic organisations, to implement the MAIHDA methods in other social science subject areas. This will include the analysis of carers, and how being a carer affects different groups of people in different ways. It will include an analysis of water quality in the United States, to see how different groups of people, in different places, are affected by poor-quality, polluted drinking water. And we will consider how educational inequalities vary for different groups of people, to see which groups are being let down by the education sector.Finally, a key part of this grant is training. We want to upskill researchers, in academia and beyond, to be able to use this method and our refinements to it in the future. As such we will produce a wide variety of online training materials, and run both online and in-person training courses, aimed at established academics, PhD students, and non-academic research organisations. These materials will be developed with other stakeholders to ensure that they meet the needs of those organisations and individuals.