Universidad Loyola Andalucía, Department of Psychology, C/ Energía Solar 1, 41014 Seville, Spain.
Universidad Loyola Andalucía, Department of Quantitative Methods, C/ Energía Solar 1, 41014 Seville, Spain.
Int J Environ Res Public Health. 2019 Jan 25;16(3):332. doi: 10.3390/ijerph16030332.
Mental health services and systems (MHSS) are characterized by their complexity. Causal modelling is a tool for decision-making based on identifying critical variables and their causal relationships. In the last two decades, great efforts have been made to provide integrated and balanced mental health care, but there is no a clear systematization of causal links among MHSS variables. This study aims to review the empirical background of causal modelling applications (Bayesian networks and structural equation modelling) for MHSS management. The study followed the PRISMA guidelines (PROSPERO: CRD42018102518). The quality of the studies was assessed by using a new checklist based on MHSS structure, target population, resources, outcomes, and methodology. Seven out of 1847 studies fulfilled the inclusion criteria. After the review, the selected papers showed very different objectives and subjects of study. This finding seems to indicate that causal modelling has potential to be relevant for decision-making. The main findings provided information about the complexity of the analyzed systems, distinguishing whether they analyzed a single MHSS or a group of MHSSs. The discriminative power of the checklist for quality assessment was evaluated, with positive results. This review identified relevant strategies for policy-making. Causal modelling can be used for better understanding the MHSS behavior, identifying service performance factors, and improving evidence-informed policy-making.
心理健康服务和系统（MHSS）的特点是其复杂性。因果建模是一种基于识别关键变量及其因果关系的决策工具。在过去的二十年中，为提供综合和平衡的精神卫生保健做出了巨大努力，但 MHSS 变量之间的因果关系没有明确的系统化。本研究旨在回顾 MHSS 管理中因果建模应用（贝叶斯网络和结构方程建模）的实证背景。该研究遵循 PRISMA 指南（PROSPERO：CRD42018102518）。使用基于 MHSS 结构、目标人群、资源、结果和方法的新清单评估研究质量。1847 项研究中有 7 项符合纳入标准。审查后，选定的论文显示出非常不同的目标和研究对象。这一发现似乎表明因果建模有可能与决策相关。主要发现提供了关于分析系统复杂性的信息，区分了它们是分析单个 MHSS 还是一组 MHSS。检查表用于质量评估的鉴别力进行了评估，结果为阳性。本综述确定了相关的决策策略。因果建模可用于更好地了解 MHSS 的行为，识别服务绩效因素，并改进基于证据的决策。
