The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking
The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking
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DOI：
10.1007/s42001-020-00067-8
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发表时间：
2021-05-01
影响因子：
3.2
通讯作者：
Zimmerman, Cathy
中科院分区：
文献类型：
--
作者：
Kiss, Ligia;Fotheringhame, David;Zimmerman, Cathy
复杂系统和现实主义评价为评价社会干预提供了有前途的方法。这些方法考虑到产生结果的各种因素之间的复杂相互作用，而不是试图孤立观察到的影响的单一原因。本文探讨了贝叶斯网络（BN）在现实主义评估干预措施，以防止复杂的社会问题。它借鉴了对自由工作方案进行的基于理论的评估的例子，该方案是国际劳工组织在南亚开展的一项由联合王国资助的大型反贩运干预行动。我们使用BN来探索因果途径，人口贩运使用的数据来自519尼泊尔难民移民。调查结果表明，贩运风险主要取决于移民的目的地国、他们是如何被招募的以及他们在哪个部门工作。这些发现挑战了人们普遍持有的关于个人层面脆弱性的假设，并强调未来的投资将受益于认识到社会背景下干预因果机制复杂性的方法。BN是一个有用的方法概念化，设计和评价复杂的社会干预。
Complex systems and realist evaluation offer promising approaches for evaluating social interventions. These approaches take into account the complex interplay among factors to produce outcomes, instead of attempting to isolate single causes of observed effects. This paper explores the use of Bayesian networks (BNs) in realist evaluation of interventions to prevent complex social problems. It draws on the example of the theory-based evaluation of the Work in Freedom Programme (WIF), a large UK-funded anti-trafficking intervention by the International Labour Organisation in South Asia. We used BN to explore causal pathways to human trafficking using data from 519 Nepalese returnee migrants. The findings suggest that risks of trafficking are mostly determined by migrants' destination country, how they are recruited and in which sector they work. These findings challenge widely held assumptions about individual-level vulnerability and emphasize that future investments will benefit from approaches that recognise the complexity of an intervention's causal mechanisms in social contexts. BNs are a useful approach for the conceptualisation, design and evaluation of complex social interventions.