An initial search should be structured relatively broadly, just focusing on two or three core concepts (often the P and the I of a PICO question).
As you search, you will begin to generate synonyms and/or alternate spellings for your concepts of interest. Pay close attention to the articles you find that appear to be most relevant - what language do they use to describe these concepts? In order to perform a comprehensive search, it is important to keep track of these synonyms.
As the search evolves, you will begin to notice that some terms are more fruitful than others; building a concept table is a useful way to track search terms, enabling you to document which terms work well and which terms should be eliminated from the strategy.
| Concept 1: diabetes | Concept 2: telehealth | |
|---|---|---|
|
Keywords/Synonyms |
type II diabetes type 2 diabetes diabetes mellitus |
telehealth mobile app cell phone |
Searching for the core concepts as keywords or text words is often a good place to start your search. A text word search will retrieve records where the search term appears anywhere in the database record for that article (eg., the authors’ names, the publication title, or the abstract).
However, you will begin to notice that database records are tagged with a controlled vocabulary to designate the subjects discussed in the full articles. These are referred to as index terms or subject headings. They are considered a controlled vocabulary because they are drawn from standardized thesauri that establish definitions and preferred usages; these terms may differ from the natural language text words you initially choose to describe a concept.
For example, indexing in MEDLINE (PubMed) employs Medical Subject Headings (MeSH). Upon being added to the database, an article about type II diabetes would be tagged with the MeSH term “Diabetes Mellitus, Type 2”. Most article databases allow you to use field tags to search specifically for records that have been tagged with a controlled subject heading, as opposed to performing a keyword or text word search.
In contrast to a text word search (where the query returns citations if the term appears anywhere in the record), a subject heading search is much more targeted, only returning results where the search term appears as the subject of that article. Consider the following example from PubMed:
Field tags can be used in a database search query to specify which field of the record you would like the search to 'look' in for your term. If a field is not specified using a field tag, the term is often searched in all fields of the record, although this setting varies from database to database. For more information about using field tags in the health databases see:
A comprehensive search is usually achieved by searching for a combination of subject headings and text words, so a strategic searcher would be well-advised to keep track of both.
| Concept 1: diabetes | Concept 2: telehealth | |
|---|---|---|
|
Keywords/Synonyms |
type II diabetes type 2 diabetes diabetes mellitus |
telehealth mobile app cell phone |
| MeSH Terms (PubMed) | “Diabetes Mellitus, Type 2” | Telemedicine “Remote Consultation” Smartphone “Mobile Applications” “Text Messaging” |
Also see:
Boolean operators (also called connectors) allow you to specify how you would like a database search platform to handle the terms of your search.
Connecting search terms with AND tells the database to return records only if all those terms appear in the record. The operator AND is typically used to connect conceptually distinct terms (such as the P and the I from a PICO question)
Connecting additional terms with AND creates a narrower search, as there will be a smaller number of records that contain the required terms.
| Search Query: | Number of Results Returned: |
|---|---|
| type II diabetes | 166,000 |
| type II diabetes AND telehealth | 556 |
| type II diabetes AND telehealth AND blood glucose | 195 |
Connecting search terms with OR tells the database to return records if any of the given terms appear in the record. Connecting synonyms and alternate terms with OR expands the search results ("OR retrieves MORE").
| Search Query: | Number of Results Returned: |
|---|---|
| telehealth | 6,100 |
| telehealth OR telemedicine | 33,000 |
| telehealth OR telemedicine OR mobile application | 51,000 |
Connecting search terms with NOT tells the database to exclude any records that contain the specified term. Adding an excluded term with NOT will create a narrower, more specific search.
| Search Query: | Number of Results Returned: |
|---|---|
| telemedicine | 36,000 |
| telemedicine NOT video | 33,000 |
*Caution: use the NOT operator sparingly and carefully; you could accidentally exclude records that are actually relevant to your search. For instance, in the example above, even if you aren't interested in seeing articles that talk about video-based telemedicine interventions, if you restrict that word completely, you will miss articles just because they contain the word 'video' somewhere in the abstract.
A complex search can be built by using the AND and OR operators together. Separate concepts can be connected with AND, while synonyms for those concepts can be connected with OR. It is important to note that synonymous terms connected with OR should be nested within parentheses, so that the database understands to keep those terms together as a set.
| Search Query: | Number of Results Returned: |
|---|---|
| type II diabetes | 166,000 |
| type II diabetes AND telehealth | 556 |
| type II diabetes AND (telehealth OR telemedicine OR mobile application) | 700 |
Using specific punctuation as wildcard or truncation characters allows you to build a more flexible search by accounting for unknown characters, multiple spellings or various endings.
Many databases use an asterisk * as the wildcard punctuation. For instance, if you're interested in allergies, a search for that exact word will only return records with the word allergies. But searching for allerg* will be much broader, capturing records that contain the words allergy OR allergies OR allergen OR allergic etc.
| Search Query: | Number of Results Returned: |
| allergies | 21,000 |
| allerg* | 33,000 |
Different database platforms allow for different truncation and wildcard punctuation. See these resources for the specifics related to common platforms:
Using specific punctuation or commands to look for exact phrases allows you to build a more specific search.
Many databases use quotation marks as the phrase searching punctuation. For instance, if you were interested in finding records that talk about the glycemic index, you could locate records with those specific words, in that exact order (rather than the words separately).
| Search Query: | Number of Results Returned: |
| glycemic index | 16,000 |
| "glycemic index" | 6,000 |
Different database platforms may allow for different phrase searching functions. See these resources for the specifics related to common platforms:
