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Advancing complexity science in healthcare research: the logic of logic models.

ABSTRACT:

Background

Logic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context. This paper assesses the logic models used in healthcare research from a complexity perspective. A typology of existing logic models is proposed, as well as a formal methodology for deriving more flexible and dynamic logic models.

Analysis

Various logic model types were tested as part of an evaluation of a complex Patient Experience Toolkit (PET) intervention, developed and implemented through action research across six hospital wards/departments in the English NHS. Three dominant types of logic model were identified, each with certain strengths but ultimately unable to accurately capture the dynamics of PET. Hence, a fourth logic model type was developed to express how success hinges on the adaption of PET to its delivery settings. Aspects of the Promoting Action on Research Implementation in Health Services (PARIHS) model were incorporated into a traditional logic model structure to create a dynamic "type 4" logic model that can accommodate complex interventions taking on a different form in different settings.

Conclusion

Logic models can be used to model complex interventions that adapt to context but more flexible and dynamic models are required. An implication of this is that how logic models are used in healthcare research may have to change. Using logic models to forge consensus among stakeholders and/or provide precise guidance across different settings will be inappropriate in the case of complex interventions that adapt to context. Instead, logic models for complex interventions may be targeted at facilitators to enable them to prospectively assess the settings they will be working in and to develop context-sensitive facilitation strategies. Researchers should be clear as to why they are using a logic model and experiment with different models to ensure they have the correct type.

SUBMITTER: Mills T

PROVIDER: S-EPMC6419426 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Advancing complexity science in healthcare research: the logic of logic models.

Mills Thomas T Lawton Rebecca R Sheard Laura L

BMC medical research methodology 20190312 1

<h4>Background</h4>Logic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context. This paper assesses the logic models used in healthcare research from a complexity perspective. A typology of existing logic models is proposed, as well as a formal methodology for deriving more flexible and dynamic logic models. ...[more]

PMID: 30871474

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Project description:OBJECTIVES:To analyse trends in the academic literature applying complexity science to healthcare, focusing specifically on bibliometric characteristics and indicators of influence. DESIGN:This study reports a bibliometric analysis via a systematic search of the academic literature applying complexity science to healthcare. METHOD:A search of four academic databases was performed on 19 April 2018. Article details were downloaded and screened against inclusion criteria (peer-reviewed journal articles applying complexity science to healthcare). Publication and content data were then collected from included articles, with analysis focusing on trends over time in the types and topics of articles, and where they are published. We also analysed the influence of this body of work through citation and network analyses. RESULTS:Articles on complexity science in healthcare were published in 268 journals, though a much smaller subset was responsible for a substantial proportion of this literature. USA contributed the largest number of articles, followed by the UK, Canada and Australia. Over time, the number of empirical and review articles increased, relative to non-empirical contributions. However, in general, non-empirical literature was more influential, with a series of introductory conceptual papers being the most influential based on both overall citations and their use as index references within a citation network. The most common topics of focus were health systems and organisations generally, and education, with recent uptake in research, policy, and change and improvement. CONCLUSIONS:This study identified changes in the types of articles on complexity science in healthcare published over time, and their content. There was evidence to suggest a shift from conceptual work to the application of concrete improvement strategies and increasingly in-depth examination of complex healthcare systems. We also identified variation in the influence of this literature at article level, and to a lesser extent by topic of focus.

Project description:Increasing recognition of the complexity of environmental problems and the need to understand social processes and human values is leading environmental management agencies in many nations, including the USA, to integrate more research from the social sciences through the inclusion of social scientists on interdisciplinary teams. For this study we conducted focus groups at three research laboratories within the U.S. Environmental Protection Agency's Office of Research and Development to better understand how inclusion of social sciences influenced the research process and outcomes, and the barriers to and facilitators of integration. The focus groups identified effects on the research process including improved problem framing, the introduction of new methodologies, and greater stakeholder and public inclusion, while research outcomes included the inclusion or refinement of social and environmental perspectives and systems thinking, increased translatability of research, and new partnerships. Barriers identified included lack of familiarity with social sciences which affected perceptions of social sciences and organizational capacity to absorb and apply social science expertise. Facilitators included receptivity of team members, intentional communication strategies, and project structures and organizational commitment that support interdisciplinary work. Finding a key barrier to be lack of clarity about the different roles social sciences play in translational research, we present a conceptual model defining the roles and contributions of social scientists that clarifies the distinction between "integration" of social sciences in research and "application" of skills and knowledge from the social sciences which play distinct but equally important roles in translational research approaches and solutions-driven research. These insights on the ways social sciences contribute to translational research efforts advance integration of social and natural sciences in environmental science research, particularly in applied contexts.

Project description:BackgroundHealth Research Priority Setting (HRPS) in the Ministry of Health (MOH) Malaysia was initiated more than a decade ago to drive effort toward research for informed decision and policy-making. This study assessed the impact of funded prioritised research and identified research gaps to inform future priority setting initiatives for universal access and quality healthcare in Malaysia.MethodsResearch impact of universal access and quality healthcare projects funded by the National Institutes of Health Malaysia were assessed based on the modified Payback Framework, addressing categories of informing policy, knowledge production, and benefits to health and health sector. For the HRPS process, the Child Health and Nutrition Research Initiative methodology was adapted and adopted, with the incorporation of stakeholder values using weights and monetary allocation survey. Workshop discussions and interviews with stakeholders and research groups were conducted to identify research gaps, with the use of conceptual frameworks to guide the search.ResultsSeventeen ongoing and 50 completed projects were identified for research funding impact analysis. Overall, research fund allocation differed from stakeholders' expectation. For research impact, 48 out of 50 completed projects (96.0%) contributed to some form of policy-making efforts. Almost all completed projects resulted in outputs that contributed to knowledge production and were expected to lead to health and health sector benefits. The HRPS process led to the identification of research priority areas that stemmed from ongoing and new issues identified for universal access and quality healthcare.ConclusionThe concerted efforts of evaluation of research funding impact, prioritisation, dissemination and policy-maker involvement were valuable for optimal health research resource utilisation in a resource constrained developing country. Embedding impact evaluation into a priority setting process and funding research based on national needs could facilitate health research investment to reach its potential.

Project description:BackgroundMany interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts.MethodsWe used a snowball sampling approach to identify published theories that were evaluated to identify constructs based on strength of conceptual or empirical support for influence on implementation, consistency in definitions, alignment with our own findings, and potential for measurement. We combined constructs across published theories that had different labels but were redundant or overlapping in definition, and we parsed apart constructs that conflated underlying concepts.ResultsThe CFIR is composed of five major domains: intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation. Eight constructs were identified related to the intervention (e.g., evidence strength and quality), four constructs were identified related to outer setting (e.g., patient needs and resources), 12 constructs were identified related to inner setting (e.g., culture, leadership engagement), five constructs were identified related to individual characteristics, and eight constructs were identified related to process (e.g., plan, evaluate, and reflect). We present explicit definitions for each construct.ConclusionThe CFIR provides a pragmatic structure for approaching complex, interacting, multi-level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories. It can be used to guide formative evaluations and build the implementation knowledge base across multiple studies and settings.

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Advancing complexity science in healthcare research: the logic of logic models.

Background

Analysis

Conclusion

Publications

Advancing complexity science in healthcare research: the logic of logic models.

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