Pacific Northwest Chapter
of MLA
 

Details of CE Sessions at PNCMLA 2018


Morning CE

Advanced PubMed Techniques

Instructor: Rebecca Brown

This 4-hour Advanced PubMed class will cover a number of concepts that librarians can add to their PubMed toolkit. Upon completion of the session, attendees will be able to: describe the order of Automatic Term Mapping and the implications on searching; apply strategies for effective author searching; apply search strategies to find citations about treating diseases with drugs and locating cancer literature; define pharmacologic action [PA] and know when to use [PA] terms from the MeSH database; use PubMed tools to find literature that supports evidence-based medicine and explain how PubMed handles phrases found in the biomedical literature.


Searching Effectively & Efficiently 

Instructor:  Andrew Hamilton

How can we search effectively & efficiently? In this class you will learn (and practice) methods used by expert searchers. You will learn some advanced search techniques for PubMed and create a self-study plan to hone your skills.

Learning Objectives

By the end of the session, participants will be able to:

  • Translate search requests into appropriate search terms
  • Build a search strategy, modify it, and improve it
  • Outline steps to support a systematic review
  • Develop a plan for maintaining and improving search skills


Afternoon CE

Emerging Technologies for the Busy Librarian 

Instructor: Gabriel Rios

This face-to-face course is designed to give the busy librarian an overview of emerged and emerging technologies impacting our profession. It is a survey course that will cover a variety of technology topics similar to MLA's annual Tech Trends panel. Topics discussed will be updated until the month prior to the teaching of course but could include: makerspaces, augmented reality, wearable technology, fitness apps, the Internet of Things, library mobile apps, privacy, ambient intelligence, virtual reality, and collaboration tools. Students, health professionals, and consumers use technologies to interact with health information daily. It is essential for librarians to investigate and experiment with these technologies to improve access to timely and relevant quality health information.

Learning Objectives

At the end of this course, participants should be able to:

  • Identify and discuss emerged and emerging technologies with potential to impact our profession
  • Review technology trends with an emphasis how to keep up to date
  • Discuss mobile apps popular in library environments
  • Identify technologies that can be employed in a hospital library or other potentially restrictive networks
  • Review privacy issues within a digital ecosystem
  • Apply case studies and recommendations for supporting emerged and emerging technologies at home institution


Statistics 101

Instructor: Dr. Jan Dasgupta

This 4-hour Statistical Literacy class will cover a number of concepts about data that librarians will find invaluable. We will start with understanding what data is and why and how we collect it. In these days of buzz words relating to “big data”, we will talk about the basics of what data is, what it is meant to be, big or small. We will cover terms like: population, sample, parameter, statistics, exploratory studies, confirmatory studies, inference, testing and p-values. We will make a distinction between univariate, bivariate and multivariate methods. At the end of the class the attendees should have a good basic understanding of the types of data and how each type needs to be analyzed differently. The hope is that, the attendees will not be intimidated by jargon but will be able to understand some basic concepts when confronted with statistical terms. We will collect some data in class and analyze it so that the attendees get hands-on experience about the process.

The list of topics for this session are given below:

  • Statistics: The Science of Data
  • Types of Data. Why we collect data?
  • Big data: What, when and how?
  • Population versus Sample
  • Experiments, observational studies
  • Exploratory studies versus confirmatory studies
  • The Idea of inference
  • Distinction: Uni-variate, Bi-variate, Multi-variate, multiple (with example data sets)
  • Graphical Summary
  • Numerical summary
  • From center to one sample tests
  • The idea of testing and p-values
  • Controversy with p-values
  • Summary and Discussion


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