Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review.

Clicks: 403
ID: 90525
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #62 of 862 articles by views in International journal of environmental research and public health

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 862 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
almeda2019causalinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Almeda, Nerea;García-Alonso, Carlos R;Salinas-Pérez, José A;Gutiérrez-Colosía, Mencía R;Salvador-Carulla, Luis;
Journal International journal of environmental research and public health
Year 2019
DOI
E332
URL
Keywords
systematic review mental health services planning bayesian networks mental health systems structural equation modelling causal model mental health care, management policy-making

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.