The Unofficial Guide to Medical Research, Audit and Teaching, Kartoniert / Broschiert

Klappentext

The unique and award-winning Unofficial Guides series is a collaboration between senior students, junior doctors and specialty experts. This combination of contributors understands what is essential to excel on your course, in exams and in practice - as well as the importance of presenting information in a clear, fun and engaging way. Packed with hints and tips from those in the know, when you are in a hurry and need a study companion you can trust, reach for an Unofficial Guide.

This unique book provides a succinct introduction to how to pursue research, teaching or audit opportunities. You’ll find guidance on how to do a literature search, key concepts in statistics, and tips for science communication, critical appraisal and clinical governance - all written in a concise way that’s perfect for undergraduates.

Fully updated for its second edition, the Unofficial Guide to Medical Research, Audit and Teaching will be a day-to-day reference you use long after you’ve graduated.
Step by step guide covering everything you need to know to undertake research, audit or teaching - easily accessible and all in one place
Clearly laid out and easy to understand
Practical tips and tricks - covers not just what you need to do, but also how to do it
Includes a concise guide to medical statistics
Convenient checklists for analysing the different types of research studies
Guidance on finding research opportunities and how to approach a supervisor
Tips for effective teaching
Guidance for portfolios

Updated information regarding specialised foundation programme
Advice on working abroad - including Australia and New Zealand
Information regarding hierarchy of evidence and n=1 studies
Updated examples to explain statistical analysis techniques
Introduction to clinical commissioning and policy development
Use of social media and online communities for science communication
Digital teaching
New and emerging research areas including computational modelling