Preface

This is the first step towards creating a book for Clinical Inquiry based on a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook.

Clinical Inquiry: Integration for Practice is a work in progress that presents a system of clinical logic, epistemology and reasoning (CLEaR) founded on the belief that it is necessary to develop theory and practice in conjunction not in isolation. A clinical inquiry that provides integration for practice. While thinking about these topics has been a career long endeavor, writing about it started in 2015 as a blog by the author called Knowledge Based Practice: Cause, Models and Inference. Between 2015 and 2020 the ideas were put into practice as a Doctor of Physical Therapy (DPT) program at Plymouth State University (PSU). In its current form it is a “living” document in development. As a system Clinical Inquiry aims to provide support for intellectual development and better communication of knowledge for practice.

0.1 About the (future) cover

The system herein referred to as Clinical Inquiry as a whole can be compared to an ice berg. Much of the system is under the water (theory) supporting the ice above the water (practice) which is what most physical therapists think about and need daily to make decisions. But without the ice under the water, there is no support for the ice above the water; therefore for growth of the ice above the water we need growth under the water.

0.2 About the DPT program at PSU

The DPT program at PSU includes a course sequence starting with a course called Clinical Inquiry I which covers the tools that support clinical reasoning and its development such as the connections between generating and utilizing knowledge in practice. These concepts are threaded through several learning experiences that are intended to help students learn principles of clinical reasoning (logical and epistemological foundations). Examples of these experiences include encoding the causal structure of relevant clinical variables in a causal model, using algorithms and broad concepts to guide an evaluation, and intentionally reflecting on clinical experiences as stories that have a causal structure.