scholarly journals Measuring implementation climate: psychometric properties of the Implementation Climate Scale (ICS) in Norwegian mental health care services

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Nadina Peters ◽  
Randi Hovden Borge ◽  
Ane- Marthe Solheim Skar ◽  
Karina M. Egeland

AbstractBackgroundEmployees’ perceptions of organizational climate for implementation of new methods are important in assessing and planning for implementation efforts. More specifically, feedback from employees’ points to which implementation strategies to select, adopt, and tailor in building positive climate for implementation of new evidence-based practices within the organization. Implementation climate can be measured with the Implementation Climate Scale (ICS). The purpose of this study was to investigate the psychometric properties of the Norwegian version of the ICS in outpatient mental health clinics.MethodsThe ICS was administered to 383 clinicians within 47 different child and adult mental health clinics across the country. We conducted confirmatory factor analysis to assess the psychometric functioning of the ICS. Cronbach’s alpha was examined to assess internal consistency. We also examined criterion related validity of the scale by comparing it with an alternative measure of implementation climate (concurrent validity) and by examining correlations with clinicians’ intentions to use evidence-based practices.ResultsResults supported the 6-factor structure and the internal consistency reliability of the ICS. One exception was poor functioning of the Reward scale. Concurrent validity was stronger at the group than at the individual level, and assessment of associations with clinicians’ intentions to use evidence- based practices showed positive correlations.ConclusionsThe Norwegian version of the ICS is a promising tool for assessing implementation climate which can provide organizations with specific feedback concerning which aspects of the implementation climate to attend to. Due to poor functioning of the Reward scale, adaptations and further testing of this is recommended.

2016 ◽  
Vol 67 (7) ◽  
pp. 710-717 ◽  
Author(s):  
Rinad S. Beidas ◽  
Rebecca E. Stewart ◽  
Courtney Benjamin Wolk ◽  
Danielle R. Adams ◽  
Steven C. Marcus ◽  
...  

2021 ◽  
Vol 2 ◽  
pp. 263348952110188
Author(s):  
Byron J Powell ◽  
Kayne D Mettert ◽  
Caitlin N Dorsey ◽  
Bryan J Weiner ◽  
Cameo F Stanick ◽  
...  

Background: Organizational culture, organizational climate, and implementation climate are key organizational constructs that influence the implementation of evidence-based practices. However, there has been little systematic investigation of the availability of psychometrically strong measures that can be used to assess these constructs in behavioral health. This systematic review identified and assessed the psychometric properties of measures of organizational culture, organizational climate, implementation climate, and related subconstructs as defined by the Consolidated Framework for Implementation Research (CFIR) and Ehrhart and colleagues. Methods: Data collection involved search string generation, title and abstract screening, full-text review, construct assignment, and citation searches for all known empirical uses. Data relevant to nine psychometric criteria from the Psychometric and Pragmatic Evidence Rating Scale (PAPERS) were extracted: internal consistency, convergent validity, discriminant validity, known-groups validity, predictive validity, concurrent validity, structural validity, responsiveness, and norms. Extracted data for each criterion were rated on a scale from −1 (“poor”) to 4 (“excellent”), and each measure was assigned a total score (highest possible score = 36) that formed the basis for head-to-head comparisons of measures for each focal construct. Results: We identified full measures or relevant subscales of broader measures for organizational culture ( n = 21), organizational climate ( n = 36), implementation climate ( n = 2), tension for change ( n = 2), compatibility ( n = 6), relative priority ( n = 2), organizational incentives and rewards ( n = 3), goals and feedback ( n = 3), and learning climate ( n = 2). Psychometric evidence was most frequently available for internal consistency and norms. Information about other psychometric properties was less available. Median ratings for psychometric properties across categories of measures ranged from “poor” to “good.” There was limited evidence of responsiveness or predictive validity. Conclusion: While several promising measures were identified, the overall state of measurement related to these constructs is poor. To enhance understanding of how these constructs influence implementation research and practice, measures that are sensitive to change and predictive of key implementation and clinical outcomes are required. There is a need for further testing of the most promising measures, and ample opportunity to develop additional psychometrically strong measures of these important constructs. Plain Language Summary Organizational culture, organizational climate, and implementation climate can play a critical role in facilitating or impeding the successful implementation and sustainment of evidence-based practices. Advancing our understanding of how these contextual factors independently or collectively influence implementation and clinical outcomes requires measures that are reliable and valid. Previous systematic reviews identified measures of organizational factors that influence implementation, but none focused explicitly on behavioral health; focused solely on organizational culture, organizational climate, and implementation climate; or assessed the evidence base of all known uses of a measure within a given area, such as behavioral health–focused implementation efforts. The purpose of this study was to identify and assess the psychometric properties of measures of organizational culture, organizational climate, implementation climate, and related subconstructs that have been used in behavioral health-focused implementation research. We identified 21 measures of organizational culture, 36 measures of organizational climate, 2 measures of implementation climate, 2 measures of tension for change, 6 measures of compatibility, 2 measures of relative priority, 3 measures of organizational incentives and rewards, 3 measures of goals and feedback, and 2 measures of learning climate. Some promising measures were identified; however, the overall state of measurement across these constructs is poor. This review highlights specific areas for improvement and suggests the need to rigorously evaluate existing measures and develop new measures.


2021 ◽  
Vol 2 ◽  
pp. 263348952110106
Author(s):  
Wanyu Huang ◽  
Chia-Hsiu Chang ◽  
Elizabeth A Stuart ◽  
Gail L Daumit ◽  
Nae-Yuh Wang ◽  
...  

Background: Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this article is to explain how agent-based modeling could fulfill this role. Methods: We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. Results: For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers, and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. Conclusion: In this article, we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components. Plain Language Summary: The goal of this paper is to explain how agent-based modeling could be used as a supplementary tool to support the components of complex implementation processes. Such models have not yet been widely used in implementation science, partly because they are not straightforward to develop. To promote the use of agent-based modeling we provide a stepwise procedure using non-technical language and emphasizing the relationships between the model and implementation processes. We used two detailed examples to demonstrate our proposed approach. In the first example, we simulate the observed smoking behaviors of persons with serious mental illness in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with Serious Mental Illness). In the second example, we illustrate how agent-based models that include interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. For this example, we show how the visual user interface of an agent-based model can be in the form of a dashboard with levers for simulating what-if scenarios that can be used to guide implementation decisions. In summary, this paper shows how agent-based models can provide insights into the processes in complex interventions, and guide implementation decisions for improving delivery of evidence-based practices in community mental health clinics.


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