SynthesisInternational journal of environmental research and public health2019
Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review.
Synthesis in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of the international evolution of online mental health strategies and recommendations during the COVID-19 pandemic.BMC psychiatry · 2022Pooled it
- Mental health planning at a very early stage of the COVID-19 crisis: a systematic review of online international strategies and recommendations.BMC psychiatry · 2021Pooled it
- An Evaluation Protocol for A Stabilisation and Referral Area (SARA): A Novel Short Stay Psychiatry Unit Serving A Remote Region of Australia.Alpha psychiatry · 2025Article
- A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas.Journal of research in health sciences · 2023Article
- Acute psychiatric care: approaches to increasing the range of services and improving access and quality of care.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2022Article
- Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem.PloS one · 2022Article
- Using linked data to identify pathways of reporting overdose events in British Columbia, 2015-2017.International journal of population data science · 2022Article
- Assessing and Improving the Quality in Mental Health Services.International journal of environmental research and public health · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.