Controlled vocabulary
Controlled vocabulary is a term that librarians and information professionals use to describe the official, accepted language of a specific database. For instance, the controlled vocabulary of PubMed is called MeSH (Medical Subject Headings), while the controlled vocabulary of CINAHL is called Subject Terms. Controlled vocabulary terms are occasionally different from one another across databases. For instance, PubMed uses the MeSH term "Sexual and Gender Minorities," while CINAHL uses the Subject Term "GLBT Persons."
Keywords
Keywords are the unofficial terms that are used to describe a concept. These may be plain language terms or highly specialized jargon. They are different from controlled vocabulary because they are not the words used by the database to organize resources. For instance, PsycINFO uses the Subject Term "Neoplasms," but the literature also uses the keyword "cancer."
Synonyms
Synonyms are words that are interchangeable with one another in describing a concept. For instance, "heart attack" and "myocardial infarction" are used interchangeably within the literature to describe an event when the heart stops beating.
Related Terms
Related terms are words that may not be perfectly interchangeable with others, but they may "play nicely" in the literature. These terms may be similar to the original keywords and may appear in conjunction with the original keywords within the literature. For instance, when searching for "mindfulness," you may also see related terms such as "yoga" and "deep breathing."
Boolean Operators (OR, AND)
Boolean operators are commands to the database, instructing the database how to interpret your combination of search terms. Use the OR operator to search all synonyms and related terms within a concept group. Use the AND operator to narrow the search to only the literature that discusses all of your concept groups together.
The PICO(TS) Method is used to take a research question and break it down into searchable concept groups. The acronym stands for:
P = patient, population, problem, phenomenon
I = intervention, exposure
C = comparison
O = outcome
T = timeframe
S = setting
There are many different ways to use this method, and there are no restrictions on how many concepts can be within each letter category. (For instance, you might have a patient and a problem.) I would caution against searching for outcomes; this could potentially cause selection bias in your search results where you only find results to support your hypothesis or argument.
P = nurses; health literacy
I = none
C = none
O = none
T = none
S = rural
Using concept tables can help you to group controlled vocabulary, keywords, synonyms, and related terms within a concept group. If I have three concept groups, for instance, then I start with a table of 3 rows and 4 columns (see below). In this example, I am using the CINAHL Subject Terms. MH (" ") is the command that tells CINAHL to treat this word as a Subject Term. See Exploring: CINAHL page for more information.
| Concept 1: Nurses |
Concept 2: Health literacy |
Concept 3: Rural areas |
|
|---|---|---|---|
| Subject Terms | (MH "Nurses") | (MH "Health Literacy") |
(MH "Rural Areas") |
| Keywords | Nurse Nurses "Nursing personnel" |
"Health literacy" | Rural "Rural areas" "Rural health centers" "Rural population" "Rural populations" "Rural health nursing" "Underserved populations" |
Tip: Make a new concept table for each database. You should be searching all of your keywords across all of the databases, but you will need to change your controlled vocabulary according to the recommendation of each database.
The image above offers a visual description of what we do when we transform our concept table into a search strategy.
(all words related to Concept 1), (all words related to Concept 2), (all words related to Concept 3)
(Official term OR Synonym OR Related term)
(Concept 1 Official term OR Synonym OR Related term)
AND
(Concept 2 Official term OR Synonym OR Related term)
AND
(Concept 3 Official term OR Synonym OR Related term)
Tip: use double quotation marks (" ") around search terms of more than one word. So, instead of searching for nursing personnel, search for "nursing personnel". This commands the database to keep your words together as a single unit.
Using the example search topic from above, the concept table becomes the following:
In the database, it looks like this:
((MH "Nurses") OR Nurse OR Nurses OR "Nursing personnel") AND ((MH "Health Literacy") OR "Health literacy") AND ((MH "Rural Areas") OR (MH "Rural Health") OR (MH "Rural Population") OR (MH "Rural Health Personnel") OR (MH "Rural Health Centers") OR (MH "Rural Health Nursing") OR Rural OR "Rural areas" OR "Rural health centers" OR "Rural population" OR "Rural populations" OR "Rural health nursing" OR "Underserved populations")
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Databases are normally thematic in nature, so it is important to know what kind of topic you have in order to begin to narrow down your databases options. The video above explains some differences between three common databases: PubMed, CINAHL, and ProQuest. The table below (click to enlarge) outlines the topics covered by common databases.
| PubMed | CINAHL | PsycINFO | ProQuest: Nursing & Allied Health Sciences |
ERIC |
|---|---|---|---|---|
| Diagnosis | Nursing science | Psychology | Dissertations & theses | Education statistics |
| Therapy | Patient education | Social issues | Nursing science | Education research |
| Prognosis | Nurse theory | Family roles | Alternative medicine | |
| Etiology | Health administration | Behavioral science |
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