Using Network Analysis to Understand Knowledge Mobilization in a Community-based Organization

Background Knowledge mobilization (KM) has been described as putting research in the hands of research users. Network analysis is an empirical approach that has potential for examining the complex process of knowledge mobilization within community-based organizations (CBOs). Yet, conducting a network analysis in a CBO presents challenges. Purpose The purpose of this paper is to demonstrate the value and feasibility of using network analysis as a method for understanding knowledge mobilization within a CBO by (1) presenting challenges and solutions to conducting a network analysis in a CBO, (2) examining the feasibility of our methodology, and (3) demonstrating the utility of this methodology through an example of a network analysis conducted in a CBO engaging in knowledge mobilization activities. Method The final method used by the partnership team to conduct our network analysis of a CBO is described. Results An example of network analysis results of a CBO engaging in knowledge mobilization is presented. In total, 81 participants completed the network survey. All of the feasibility benchmarks set by the CBO were met. Results of the network analysis are highlighted and discussed as a means of identifying (1) prominent and influential individuals in the knowledge mobilization process and (2) areas for improvement in future knowledge mobilization initiatives. Conclusion Findings demonstrate that network analysis can be feasibly used to provide a rich description of a CBO engaging in knowledge mobilization activities

Assessing Connections Between Behavior Change Theories Using Network Analysis

A cross-disciplinary scoping review identified 83 of behavior change theories, with many similarities and overlapping constructs. Investigating the derivation of these theories may provide further understanding of their contribution and intended application

Developing interventions to change recycling behaviors: A case study of applying behavioral science

The Theoretical Domains Framework (TDF) and the Behavior Change Wheel (BCW) are frameworks that can be used to develop recycling interventions. The aim of this study was to demonstrate the utility of these frameworks for developing recycling interventions. 20 semistructured interviews with university building users were analyzed using the TDF and BCW. Environmental context and resources, beliefs about consequences, knowledge, and intention were identified as the key theoretical domains influencing recycling behaviors. The BCW was used to develop recommendations for intervention. This research is the first case study to demonstrate how the TDF and the BCW can be used to develop recycling interventions

Associations between Practitioner Personality and Client Quit Rates in Smoking Cessation Behavioural Support Interventions

Introduction: There is wide variation in the success rates of practitioners employed to help smokers to stop, even once a range of potential confounding factors has been taken into account. Aim: This paper examined whether personality characteristics of practitioners might play a role success rates. Methods: Data from 1,958 stop-smoking treatment episodes in two stop-smoking services (SSS) involving 19 stop-smoking practitioners were used in the analysis. The outcome measure was clients’ biochemically verified quit status 4 weeks after the target quit date. The five dimensions of personality, as assessed by the Ten-Item Personality Inventory, were included as predictor variables: openness, conscientiousness, agreeableness, extraversion, and neuroticism. A range of client and other practitioner characteristics were used as covariates. A sensitivity analysis was conducted to determine if managers' ratings of practitioner personality were also associated with clients’ quit status. \ud Results: Multi-level random intercept models indicated that clients of practitioners with a higher extraversion score had greater odds of being abstinent at four weeks (self-assessed: OR = 1.10, 95% CI = 1.01–1.19; manager-assessed: OR = 1.32, 95% CI = 1.21–1.44). Conclusions: More extraverted stop smoking practitioners appear to have greater success in advising their clients to quit smoking. Findings need to be confirmed in larger practitioner populations, other SSS, and in different smoking cessation contexts. If confirmed, specific training may be needed to assist more introverted stop smoking practitioners

AIMD - A validated, simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies

Background: Proliferation of terms describing the science of effectively promoting and supporting the use of research evidence in healthcare policy and practice has hampered understanding and development of the field. To address this, an international Terminology Working Group developed and published a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. This paper presents results of validation work and a second international workgroup meeting, culminating in the updated AIMD framework [Aims, Ingredients, Mechanism, Delivery]. Methods: Framework validity was evaluated against terminology schemas (n = 51); primary studies (n = 37); and reporting guidelines (n = 10). Framework components were independently categorized as fully represented, partly represented, or absent by two researchers. Opportunities to refine the framework were systematically recorded. A meeting of the expanded international Terminology Working Group updated the framework by reviewing and deliberating upon validation findings and refinement proposals. Results: There was variation in representativeness of the components across the three types of literature, in particular for the component 'causal mechanisms'. Analysis of primary studies revealed that representativeness of this concept lowered from 92 to 68% if only explicit, rather than explicit and non-explicit references to causal mechanisms were included. All components were very well represented in reporting guidelines, however the level of description of these was lower than in other types of literature. Twelve opportunities were identified to improve the framework, 9 of which were operationalized at the meeting. The updated AIMD framework comprises four components: (1) Aims: what do you want your intervention to achieve and for whom? (2) Ingredients: what comprises the intervention? (3) Mechanisms: how do you propose the intervention will work? and (4) Delivery: how will you deliver the intervention? Conclusions: The draft simplified framework was validated with reference to a wide range of relevant literature and improvements have enhanced useability. The AIMD framework could aid in the promotion of evidence into practice, remove barriers to understanding how interventions work, enhance communication of interventions and support knowledge synthesis. Future work needs to focus on developing and testing resources and educational initiatives to optimize use of the AIMD framework in collaboration with relevant end-user groups

A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems

Background: Implementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations. Methods: Researchers from Canada, the UK and Australia attended a 3-day meeting in December 2012 to build an international collaboration among researchers and decision-makers interested in the advancing use of the TDF. The participants were experienced in using the TDF to assess implementation problems, design interventions, and/or understand change processes. This guide is an output of the meeting and also draws on the a uthors' collective experience. Examples from the implementation research literature judged by authors to be representative of specific applications of the TDF are included in this guide. Results: We explain and illustrate methods, with a focus on qualitative approaches, for selecting and specifying target behaviours key to implementation, selecting the study design, deciding the sampling strategy, developing study materials, collecting and analysing data, and reporting findings of TDF-based studies. Areas for development include methods for triangulating data, e.g. from interviews, questionnaires and observation and methods for designing interventions based on TDF-based problem analysis. Conclusions: We offer this guide to the implementation community to assist in the application of the TDF to achieve implementation objectives. Benefits of using the TDF include the provision of a theoretical basis for implementation studies, good coverage of potential reasons for slow diffusion of evidence into practice and a method for progressing from theory-based investigation to intervention

Use of dynamic systems methods to characterize dyadic interactions in smoking cessation behavioural support sessions: A feasibility study

BACKGROUND: Understanding how behaviour change techniques (BCTs) operate in practice requires a method for characterizing the reciprocal, dynamic, and real-time nature of behavioural support interactions between practitioners and clients. State space grids (SSGs) are an observational, dynamic systems methodology used to map the trajectory of dyadic interactions in real time. By mapping the flow of events in terms of practitioner and client actions, SSGs are potentially well suited to characterize behavioural support sessions. PURPOSE: To develop reliable methods and examine the feasibility of using the SSG methodology for characterizing practitioners' delivery of and clients' response to BCTs in smoking cessation behavioural support sessions. METHODS: Smoking cessation behavioural support sessions were video-recorded and transcribed verbatim (n = 6 recordings; 2,916 statements). All speech was coded independently by two researchers for content and duration using published frameworks for specifying practitioner-delivered and client-received BCTs in smoking cessation interactions. Inter-rater reliability was assessed. Indices of practitioner-client interaction dynamics were derived: (1) reciprocity (i.e., attractor states, content congruence, conditional pairing) and (2) temporal patterning (i.e., variability, inter-grid distance, combinatory micro-patterning, sessional macropatterning). The extent to which indices can describe differences between sessions involving different practitioners and clients was examined. RESULTS: Inter-rater reliability was moderate at 72% agreement. Indices of reciprocity and temporal patterning characterized differences between sessions involving different practitioners and clients. CONCLUSIONS: State space grids provide a method for characterizing the complexity and variability of practitioner-delivered and client-received BCTs in behavioural support sessions. This method has potential to add explanatory value to smoking cessation intervention outcomes. Statement of Contribution What is already known on this subject? Frameworks exist for characterizing practitioner-delivered and client-received behaviour change techniques (BCTs). Methods are still needed to investigate which BCTs are effective under what conditions. State space grids (SSGs) are a dynamic systems method that may better characterize behavioural support interactions. What does this study add? First reliable, dynamic systems, SSG coding procedures, methods, and measures to characterize behavioural support. A method for examining reciprocality and temporal patterning of BCT delivery and receipt. Establishes a dynamic systems method that adds explanatory value to the outcomes of interventions