Process models

Process models are used to describe and/or guide the process of translating research into practice. Models by Huberman [40], Landry et al. [41], the CIHR (Canadian Institutes of Health Research) Knowledge Model of Knowledge Translation [42], Davis et al. [43], Majdzadeh et al. [44] and the K2A (Knowledge-to-Action) Framework [15] outline phases or stages of the research-to-practice process, from discovery and production of research-based knowledge to implementation and use of research in various settings.

Early research-to-practice (or knowledge-to-action) models tended to depict rational, linear processes in which research was simply transferred from producers to users. However, subsequent models have highlighted the importance of facilitation to support the process and placed more emphasis on the contexts in which research is implemented and used. Thus, the attention has shifted from a focus on production, diffusion and dissemination of research to various implementation aspects [21].

So-called action (or planned action) models are process models that facilitate implementation by offering practical guidance in the planning and execution of implementation endeavours and/or implementation strategies. Action models elucidate important aspects that need to be considered in implementation practice and usually prescribe a number of stages or steps that should be followed in the process of translating research into practice. Action models have been described as active by Graham et al. ([45]:185) because they are used “to guide or cause change”. It should be noted that the terminology is not fully consistent, as some of these models are referred to as frameworks, for instance the Knowledge-to-Action Framework [46].

Many of the action models originate from the nursing-led field of research use/utilization; well-known examples include the Stetler Model [47], the ACE (Academic Center for Evidence-Based Practice) Star Model of Knowledge Transformation [48], the Knowledge-to-Action Framework [13], the Iowa Model [49,50] and the Ottawa Model [51,52]. There are also numerous examples of similar “how-to-implement” models that have emerged from other fields, including models developed by Grol and Wensing [53], Pronovost et al. [54] and the Quality Implementation Framework [27], all of which are intended to provide support for planning and managing implementation endeavours.

The how-to-implement models typically emphasize the importance of careful, deliberate planning, especially in the early stages of implementation endeavours. In many ways, they present an ideal view of implementation practice as a process that proceeds step-wise, in an orderly, linear fashion. Still, authors behind most models emphasize that the actual process is not necessarily sequential. Many of the action models mentioned here have been subjected to testing or evaluation, and some have been widely applied in empirical research, underscoring their usefulness [9,55].

The process models vary with regard to how they were developed. Models such as the Stetler Model [47,56] and the Iowa Model [49,50] were based on the originators’ own experiences of implementing new practices in various settings (although they were also informed by research and expert opinion). In contrast, models such as the Knowledge-to-Action Framework [45] and the Quality Implementation Framework [27] have relied on literature reviews of theories, models, frameworks and individual studies to identify key features of successful implementation endeavours.

Determinant frameworks

Determinant frameworks describe general types (also referred to as classes or domains) of determinants that are hypothesized or have been found to influence implementation outcomes, e.g. health care professionals’ behaviour change or adherence to a clinical guideline. Each type of determinant typically comprises a number of individual barriers (hinders, impediments) and/or enablers (facilitators), which are seen as independent variables that have an impact on implementation outcomes, i.e. the dependent variable. Some frameworks also hypothesize relationships between these determinants (e.g. [17,57,58]), whereas others recognize such relationships without clarifying them (e.g. [59,60]). Information about what influences implementation outcomes is potentially useful for designing and executing implementation strategies that aim to change relevant determinants.

The determinant frameworks do not address how change takes place or any causal mechanisms, underscoring that they should not be considered theories. Many frameworks are multilevel, identifying determinants at different levels, from the individual user or adopter (e.g. health care practitioners) to the organization and beyond. Hence, these integrative frameworks recognize that implementation is a multidimensional phenomenon, with multiple interacting influences.

The determinant frameworks were developed in different ways. Many frameworks (e.g. [17,21,22,57,59,61]) were developed by synthesizing results from empirical studies of barriers and enablers for implementation success. Other frameworks have relied on existing determinant frameworks and relevant theories in various disciplines, e.g. the frameworks by Gurses et al. [58] and CFIR (Consolidated Framework for Implementation Research) [60].

Several frameworks have drawn extensively on the originator’s own experiences of implementing new practices. For instance, the Understanding-User-Context Framework [62] and Active Implementation Frameworks [63] were both based on a combination of literature reviews and the originators’ implementation experiences. Meanwhile, PARIHS (Promoting Action on Research Implementation in Health Services) [5,64] emerged from the observation that successful implementation in health care might be premised on three key determinants (characteristics of the evidence, context and facilitation), a proposition which was then analysed in four empirical case studies; PARIHS has subsequently undergone substantial research and development work [64] and has been widely applied [65].

Theoretical Domains Framework represents another approach to developing determinant frameworks. It was constructed on the basis of a synthesis of 128 constructs related to behaviour change found in 33 behaviour change theories, including many social cognitive theories [10]. The constructs are sorted into 14 theoretical domains (originally 12 domains), e.g. knowledge, skills, intentions, goals, social influences and beliefs about capabilities [66]. Theoretical Domains Framework does not specify the causal mechanisms found in the original theories, thus sharing many characteristics with determinant frameworks.

The frameworks describe “implementation objects” in terms of research, guidelines, interventions, innovations and evidence (i.e. research-based knowledge in a broad sense). Outcomes differ correspondingly, from adherence to guidelines and research use, to successful implementation of interventions, innovations, evidence, etc. (i.e. the application of research-based knowledge in practice). The relevance of the end users (e.g. patients, consumers or community populations) of the implemented object (e.g. an EBP) is not explicitly addressed in some frameworks (e.g. [17,21,67]), suggesting that this is an area where further research is needed for better analysis of how various end users may influence implementation effectiveness.

Determinant frameworks imply a systems approach to implementation because they point to multiple levels of influence and acknowledge that there are relationships within and across the levels and different types of determinants. A system can be understood only as an integrated whole because it is composed not only of the sum of its components but also by the relationships among those components [68]. However, determinants are often assessed individually in implementation studies (e.g. [69-72]), (implicitly) assuming a linear relationship between the determinants and the outcomes and ignoring that individual barriers and enablers may interact in various ways that can be difficult to predict. For instance, there could be synergistic effects such that two seemingly minor barriers constitute an important obstacle to successful outcomes if they interact. Another issue is whether all relevant barriers and enablers are examined in these studies, which are often based on survey questionnaires, and are thus biased by the researcher’s selection of determinants. Surveying the perceived importance of a finite set of predetermined barriers can yield insights into the relative importance of these particular barriers but may overlook factors that independently affect implementation outcomes. Furthermore, there is the issue of whether the barriers and enablers are the actual determinants (i.e. whether they have actually been experienced or encountered) and the extent to which they are perceived to exist (i.e. they are more hypothetical barriers and enablers). The perceived importance of particular factors may not always correspond with the actual importance.

The context is an integral part of all the determinant frameworks. Described as “an important but poorly understood mediator of change and innovation in health care organizations” ([73]:79), the context lacks a unifying definition in implementation science (and related fields such as organizational behaviour and quality improvement). Still, context is generally understood as the conditions or surroundings in which something exists or occurs, typically referring to an analytical unit that is higher than the phenomena directly under investigation. The role afforded the context varies, from studies (e.g. [74-77]) that essentially view the context in terms of a physical “environment or setting in which the proposed change is to be implemented” ([5]:150) to studies (e.g. [21,74,78]) that assume that the context is something more active and dynamic that greatly affects the implementation process and outcomes. Hence, although implementation science researchers agree that the context is a critically important concept for understanding and explaining implementation, there is a lack of consensus regarding how this concept should be interpreted, in what ways the context is manifested and the means by which contextual influences might be captured in research.

The different types of determinants specified in determinant frameworks can be linked to classic theories. Thus, psychological theories that delineate factors influencing individual behaviour change are relevant for analysing how user/adopter characteristics affect implementation outcomes, whereas organizational theories concerning organizational climate, culture and leadership are more applicable for addressing the influence of the context on implementation outcomes.

Classic theories

Implementation researchers are also wont to apply theories from other fields such as psychology, sociology and organizational theory. These theories have been referred to as classic (or classic change) theories to distinguish them from research-to-practice models [45]. They might be considered passive in relation to action models because they describe change mechanisms and explain how change occurs without ambitions to actually bring about change.

Psychological behaviour change theories such as the Theory of Reasoned Action [79], the Social Cognitive Theory [80,81], the Theory of Interpersonal Behaviour [82] and the Theory of Planned Behaviour [83] have all been widely used in implementation science to study determinants of “clinical behaviour” change [84]. Theories such as the Cognitive Continuum Theory [85], the Novice-Expert Theory [86], the Cognitive-Experiential Self-Theory [87] and habit theories (e.g. [88,89]) may also be applicable for analysing cognitive processes involved in clinical decision-making and implementing EBP, but they are not as extensively used as the behaviour change theories.

Theories regarding the collective (such as health care teams) or other aggregate levels are relevant in implementation science, e.g. theories concerning professions and communities of practice, as well as theories concerning the relationships between individuals, e.g. social networks and social capital [14,53,90-93]. However, their use is not as prevalent as the individual-level theories.

There is increasing interest among implementation researchers in using theories concerning the organizational level because the context of implementation is becoming more widely acknowledged as an important influence on implementation outcomes. Theories concerning organizational culture, organizational climate, leadership and organizational learning are relevant for understanding and explaining organizational influences on implementation processes [21,53,57,94-101]. Several organization-level theories might have relevance for implementation science. For instance, Estabrooks et al. [14] have proposed the use of the Situated Change Theory [102] and the Institutional Theory [103,104], whereas Plsek and Greenhalgh [105] have suggested the use of complexity science [106] for better understanding of organizations. Meanwhile, Grol et al. [22] have highlighted the relevance of economic theories and theories of innovative organizations. However, despite increased interest in organizational theories, their actual use in empirical implementation studies thus far is relatively limited.

The Theory of Diffusion, as popularized through Rogers’ work on the spread of innovations, has also influenced implementation science. The theory’s notion of innovation attributes, i.e. relative advantage, compatibility, complexity, trialability and observability [107], has been widely applied in implementation science, both in individual studies (e.g. [108-110]) and in determinant frameworks (e.g. [17,58,60]) to assess the extent to which the characteristics of the implementation object (e.g. a clinical guideline) affect implementation outcomes. Furthermore, the Theory of Diffusion highlights the importance of intermediary actors (opinion leaders, change agents and gate-keepers) for successful adoption and implementation [107], which is reflected in roles described in numerous implementation determinant frameworks (e.g. [63,64]) and implementation strategy taxonomies (e.g. [111-114]). The Theory of Diffusion is considered the single most influential theory in the broader field of knowledge utilization of which implementation science is a part [115].

Implementation theories

There are also numerous theories that have been developed or adapted by researchers for potential use in implementation science to achieve enhanced understanding and explanation of certain aspects of implementation. Some of these have been developed by modifying certain features of existing theories or concepts, e.g. concerning organizational climate and culture. Examples include theories such as Implementation Climate [116], Absorptive Capacity [117] and Organizational Readiness [118]. The adaptation allows researchers to prioritize aspects considered to be most critical to analyse issues related to the how and why of implementation, thus improving the relevance and appropriateness to the particular circumstances at hand.

COM-B (Capability, Opportunity, Motivation and Behaviour) represents another approach to developing theories that might be applicable in implementation science. This theory began by identifying motivation as a process that energizes and directs behaviour. Capability and opportunity were added as necessary conditions for a volitional behaviour to occur, given sufficient motivation, on the basis of a US consensus meeting of behavioural theorists and a principle of US criminal law (which considers prerequisites for performance of specified volitional behaviours) [119]. COM-B posits that capability, opportunity and motivation generate behaviour, which in turn influences the three components. Opportunity and capability can influence motivation, while enacting a behaviour can alter capability, motivation and opportunity [66].

Another theory used in implementation science, the Normalization Process Theory [120], began life as a model, constructed on the basis of empirical studies of the implementation of new technologies [121]. The model was subsequently expanded upon and developed into a theory as change mechanisms and interrelations between various constructs were delineated [122]. The theory identifies four determinants of embedding (i.e. normalizing) complex interventions in practice (coherence or sense making, cognitive participation or engagement, collective action and reflexive monitoring) and the relationships between these determinants [123].

Evaluation frameworks

There is a category of frameworks that provide a structure for evaluating implementation endeavours. Two common frameworks that originated in public health are RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [124] and PRECEDE-PROCEED (Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development) [125]. Both frameworks specify implementation aspects that should be evaluated as part of intervention studies.

Proctor et al. [126] have developed a framework of implementation outcomes that can be applied to evaluate implementation endeavours. On the basis of a narrative literature review, they propose eight conceptually distinct outcomes for potential evaluation: acceptability, adoption (also referred to as uptake), appropriateness, costs, feasibility, fidelity, penetration (integration of a practice within a specific setting) and sustainability (also referred to as maintenance or institutionalization).

Although evaluation frameworks may be considered in a category of their own, theories, models and frameworks from the other four categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured. For instance, Theoretical Domains Framework (e.g. [127,128]), and Normalization Process Theory [129] and COM-B (e.g. [130,131]) have all been widely used as evaluation frameworks. Furthermore, many theories, models and frameworks have spawned instruments that serve evaluation purposes, e.g. tools linked to PARIHS [132,133], CFIR [134] and Theoretical Domains Framework [135]. Other examples include the EBP Implementation Scale to measure the extent to which EBP is implemented [136] and the BARRIERS Scale to identify barriers to research use [137], as well as instruments to operationalize theories such as Implementation Climate [138] and Organizational Readiness [139].