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Taschenbuch. Condition: Neu. Implementation Science | Theory and Application | Per Nilsen | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Routledge | EAN 9781032330846 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This core textbook introduces the key concepts, theories, models and frameworks used in implementation science, and supports readers applying them in research projects. The first part of the book focuses on the theory of implementation science, providing a discussion of its emergence from the evidence-based practice movement and its connections to related topics such as innovation research. It includes chapters looking at a wide range of theories, methods and frameworks currently used in implementation science, and a chapter focusing on suitable theories that could be imported from other fields. The first part also addresses strategies and outcomes of implementation and discusses how researchers can build causal pathwaysadapted to their study. The second part of the book focuses squarely on putting the theory of implementation science to work in practice, with chapters discussing research methods used in the field and how to select the most appropriate approach. This section also features severalchapterspresenting in-depth case studiesof specific applications.This multidisciplinary text is an essential resource for graduate students from a range of healthcare backgrounds taking courses on implementation science, as well as researchers from medicine, nursing, public health, allied health, economics, political science, sociology and engineering.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnThe Monte Carlo method is a numerical technique to model the probability of all possible outcomes in a process that cannot easily be predicted due to the interference of random variables. It is a technique used to understand the i.