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Database Searching

This LibGuide is meant to illustrate database structure and how to search databases according to their design. This LibGuide is meant to be directed toward searching Medical and Life Sciences databases.

The Research Question

The Research Question

Research is often conducted to answer a question. The research question must be well formulated to provide focus on a topic that is being investigated effectively. Any search in a database requires analysis of the research question. 

Identifying your concepts

Identifying Concepts

Typing the entire research question into a database search bar is not the way databases are designed to be searched. This is because the database utilizes fields and their data values for searching. For each concept, the user must find subject headings and generate keywords to search for the concepts in the research question.  

What are the primary concepts in the research question? Consider what population(s) you will be discussing in your research. Think about what the treatment(s), therapies, intervention(s), or program(s) to be examined. This process reflects the formulation of the research question itself.

The difference between subject headings and keywords

Subject headings describe the content of each item in a database. Use these headings to find relevant items on the same topic.  Searching by subject headings (e.g. MeSH, Emtree, CINHAL Subject Headings, etc.) is the most precise way to search article databases.

Keyword searching is how you typically search web search engines.  Think of important words or phrases and type them in to get results.

Here are some key points about each type of search:

Keywords

vs.

Subject Headings

  • natural language words describing your topic - good to start with
  • pre-defined "controlled vocabulary" words used to describe the content of each item (book, journal article) in a database
  • more flexible to search by - can combine together in many ways
  • less flexible to search by - need to know the exact controlled vocabulary term
  • database looks for keywords anywhere in the record - not necessarily connected together
  • database looks for subjects only in the subject heading or descriptor field, where the most relevant words appear
  • may yield too many or too few results
  • if too many results - also uses subheadings to focus on one aspect of the broader subject
  • may yield many irrelevant results
  • results usually very relevant to the topic

When you search a database and do not get the results you expect, Ask Us for advice.

Example Research Question for this Guide

Example research question:

Are anti-vaping campaigns effective interventions for e-cigarette use among high school students?

In the question above there are three major concepts: anti-vaping smoking; e-cigarettes; and high school students.

P = Population = High school students. Those between the ages typically 12-19. This may need to be further specified as the average age of a high school student varies. This is something that would need to be defined. The scope also may entail those students who are in American public high schools. 

P = Problem = e-cigarette (use).Electronic versions of cigarettes, includes vaping and vapes, may want to limit or exclude traditional nicotine sources like cigarettes that are non-electronic. 

I = Intervention = Anti-vaping campaigns. Further specifications can be elaborated such as whether they are peer-lead campaigns or education programs lead by school faculty. 

C = Comparison - None. No comparison applicable for this question, since it is only examining one intervention. You do not have to have every PICO component to have a PICO question.

O = Outcome - Question doesn't assume. In our construction of the above PICO question, the answer to the research question is never assumed. We are trying to investigate whether the pre-specified intervention is effective, and only the literature itself can tell us that after we run the search and get results.

Over the next few pages you will find out how to search for subject headings and generate keywords for each highlighted concept. The next step is to put your subject headings and keywords together with Boolean operators, which you will use to combine concepts together.