From Knowing to Doing: A framework for understanding the evidence-into-practice agenda

This overview paper aims to map out the terrain of research utilisation and evidence-based practice (RU/EBP) through examining six inter-related areas:

1. Types of knowledge. RU/EBP does not just require know-how, but also know-who and know-why. This type of knowledge is often based on more tacit understanding - such as 'craft expertise' - rather than explicitly systematic investigation.
   
2. Types of research utilisation. It is emphasised that research may be used in different ways, ranging from instrumental use that results in practical/behavioural change, to conceptual use that results in changes in understanding and attitude. Conceptual change is perhaps the most important impact that research can have long-term.
   
3. Models of the process of utilisation. The shift from a linear model of research/policy linkages ('research into practice') to a multi-dimensional model ('research in practice') is echoed in the shift from researcher-as-disseminator to practitioner-as-learner.
   
4. Conceptual frameworks. Different conceptual frameworks are often used implicitly to frame the RU/EBP problem in a specific way. The paper briefly outlines six possible conceptual frameworks: diffusion of innovations, institutional theory, managing change in organisations, knowledge management, individual learning, and organisational learning.
   
5. Main ways of intervening to increase evidence uptake. Broad-based approaches to securing long-term change face three key challenges: cultural challenges when dealing with multiple cultures; logistical challenges arising from difficulties with information systems and access to resources; and contextual challenges linked to differences in learning among different groups.
   
6. Different ways of conceptualising what RU/EBP means in practice. Four different 'types' or dimensions are suggested: i) the evidence-based problem solver, who has an individual and day-by-day, case-by-case focus; ii) the reflective practitioner, who uses observational data to learn from the past and adjust for the future; iii) system redesign, which emphasises the importance of reshaping total systems, often in a centrally driven way; iv) system adjustment, which refers to system level 'single-loop' learning.