An Introduction to the International Initiative for Mental Health Leadership (IIMHL)

Silvestri, Fran; Peters, Janet

doi:10.1108/17479886200700015

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This framework is useful for classifying models in order to know the levels at which they can have a potential impact. The majority of studies focus on analyzing the nano level, which involves patient or consumer interventions [15,16,17,18] and career and professional characteristics [19,20]. The other levels have been less frequently studied, although they are relevant for planning and management of mental health care.…”

Section: Introductionmentioning

confidence: 99%

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

Almeda

García‐Alonso

Salinas-Pérez

et al. 2019

IJERPH

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Almeda

García‐Alonso

Salinas-Pérez

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

An Introduction to the International Initiative for Mental Health Leadership (IIMHL)

Abstract: IIMHL editorial panelThe IIMHL have put together an editorial panel of international leaders to commission and write articles for future issues of the International Journal of Leadership in Public Services on behalf of the IIMHL.

Cited by 1 publication

References 0 publications

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

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

Contact Info

Product

Resources

About