Capstone and PICO Project Toolkit

1. Identify the core concepts that will make up the search criteria and brainstorm keywords that describe those concepts
2. Identify the appropriate database-specific subject headings that describe the core concepts
3. Structure the search query using the Boolean logic, punctuation and field tags that are appropriate to that database
4. Employ strategies to expand or narrow the search as necessary
5. Translate the search query into the vocabulary and syntax of other databases

Use the included links to explore each of these steps in more detail.

Identifying Core Search Concepts

While your research question (e.g., PICO question) and inclusion criteria may be very specific, to build a comprehensive search for research evidence, it is a good idea to structure the search broadly.  A comprehensive search often just includes two or three core concepts (for a PICO question, these concepts are typically the population and intervention of interest). 

For example, imagine your PICO question is:

"For adolescents with type II diabetes (P) does the use of telehealth consultations (I) compared to in-person consultations (C) improve blood sugar control (O)?

To build a comprehensive search for evidence that answers this question, you could use diabetes and telehealth as your core search concepts, looking for research that brings both these concepts together.  You would then retrieve a broad set of results that could potentially fit with your research question, and the other elements of the research question (e.g., the comparison and outcome) would then be used as screening criteria to help you identify studies for inclusion.  

Tracking Keywords and Synonyms

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.   

Tips for Selecting Keywords:

Avoid abstract or implied concepts

The database will only retrieve a record if that record contain your specified search terms, but for some abstract concepts, there is a wide variety of ways that the concept can be expressed or even implied. By using terms to try to describe those ideas in your searches, you may end up excluding relevant articles simply because their records don't include the exact word that you entered.  Some words may even introduce bias into your search. 

In particular, try to avoid using:

Relationship Words

These are words that words that describe the relationship between two topics (e.g., compare, contrast, correlation, impact, effect, causation, relationship).  Instead, consider just using keywords for the concepts you're trying to relate, and then you can evaluation the relationship that exists.

Judgment Words

These are value-laden words that imply that imply something is better or worse than something else (e.g., best, worst, pro, con, advantages, disadvantages, benefits, harms).  These words can unnecessarily introduce bias into your search; it's better to allow the database to retrieve records that meet your topic criteria overall, and then apply your own judgment as the researcher.   

Consider both synonyms and antonyms

Remember that both synonyms (same meaning) and antonyms (opposite meaning) terms can be useful to include to describe a concept.  As you're collecting synonyms, consider including terms for your concepts of interest that may be used more commonly outside of the US (e.g., physiotherapy/physical therapy; barrister/attorney; flat/apartment).

Also, it may be useful to include antonyms for your topics of interest.  For example, if you were interested in student retention, not only would you want to include synonymous terms like student persistence, or graduation, you'd also want include terms for the opposite phenomenon (e.g., student dropouts, student attrition)

Consider abbreviations and spelling variations

It's a good idea to search for both abbreviations and the full phrases for an important concept.  For example, a researcher interested in cognitive behavioral therapy would also want to use the term CBT. 

If the abbreviation for your topic is very common, and you find the abbreviation is generating lots of false hits, it may be useful to pair it with other qualifying words (e.g., intellectual property OR IP law).

Additionally, consider if your term of interest might have an alternative spelling or in British or Australian English (e.g., labor vs. labour).  See here for some common spelling variations to look out for. 

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 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:

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.

Also see:

• Using MeSH Terms in PubMed
• Using CINAHL Subject Headings 
• Using the APA Thesaurus to Find Index Terms in APA PsycNET

Keywords vs. Subject Headings

Example

Searching with Subject Headings

To build a more targeted search with subject headings, you can use a field tag (e.g., in CINAHL, the code MH) to ask the database to only show records if the subject term(s) appear in the subject field of the record.

All 24 records that are retrieved by this search will have "Surgery, Operative" and "Music Therapy" in their subject fields.  This means that they will very likely be records for articles that are about those two ideas.

Searching with Keywords

All 340 records that are retrieved by this query will have the words surgery and music therapy somewhere in the record (e.g., in the title, abstract, subject field, journal name, author supplied keyword, author affiliation).  These records may be about music therapy and surgery, or those words might just be found in the record somewhere (even if those topics are not necessarily significant to the content of the article).  

Boolean operators (also called connectors) allow you to specify how you would like a database search platform to handle the terms of your search.  

AND

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.

OR

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").

NOT*

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. 

*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.    

Complex Searching

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.  

Wildcard / Truncation

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.

Different database platforms allow for different truncation and wildcard punctuation.  See these resources for the specifics related to common platforms:

• EBSCO (CINAHL, Education Source): Searching with Wildcards in EBSCOhost
• PubMed (MEDLINE): PubMed Help - Truncating Search Terms
• PsycNET (PsycINFO): PsycNET Help - Truncation
• Ovid (EMBASE, PsycINFO, MEDLINE) - CIAPl Learning Module - Truncation, wildcards, and phrases on Ovid databases
• Web of Science: WoS Core Collection Wildcard Help

Phrase Searching

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).

Different database platforms may allow for different phrase searching functions.  See these resources for the specifics related to common platforms:

• EBSCO (CINAHL, Education Source): EBSCOHost - How are phrases searched?
• PubMed (MEDLINE): PubMed Help - Searching for a Phrase
• PsycNET (PsycINFO): PsycNET Help - Exact Phrase
• Ovid (EMBASE, PsycINFO, MEDLINE) - CIAPl Learning Module - Truncation, wildcards, and phrases on Ovid databases
• Web of Science: WoS Search Rules

Most databases allow you to include field codes, (also known as field tags, or search tags) with your search terms to specify in which field in the record you would like the database to look for that term. 

Different database platforms have different codes and structures for field tags, but some of the commonly available field tags are title, abstract, author and subject heading. 

• EBSCO Databases (CINAHL and Education Source) - two letter codes, capitalized before the search term.  For example: TI environmental racism
• PubMed - codes in square brackets, after the search term.  For example, environmental racism[TI]
• Ovid (EMBASE, PscyINFO, MEDLINE) - codes between two periods, after the search term.  For example, environmental racism.ti.

Some databases also have codes that allow you to search in multiple fields at once.  For instance, PubMed's TW (Text Word) tag allows PubMed to search for the given term in fields like the title, abstract, author supplied keywords, and subject headings.

For more specific information about available field tags by database, see:

• CINAHL - EBSCO Field Codes
• PubMed - Search Tags
• Web of Science Core Collection - Field Tags
• Ovid MEDLINE - Search Fields
• Ovid EMBASE - Search Fields
• Ovid PscyINFO - Search Fields

Unqualified Searches / No Field Tag

If you don't specify a field, you are performing an "unqualified search", and the database will default to searching multiple fields. Different databases have different default search fields, but title, abstract and subject heading fields are commonly included.  For specific information about what fields are included in unqualified searches by database, see:

• CINAHL via EBSCO - FAQs | Unqualified Searching
• PubMed - Defaults to search All Fields and performs Automatic Term Mapping
• Ovid (PsycINFO & EMBASE) - adds the .mp. (multipurpose) tag
• PsycNET - defaults to all searchable fields
• Web of Science Core Collection - defaults to all searchable fields

Searching in a comprehensive, systematic way requires authors to execute analogous searches in multiple databases, but not all databases accept the same search syntax, and most databases use different vocabulary for subject headings (or don't use subject headings at all).

As such, once a search strategy has been developed in one database, it is necessary to 'translate' it into a form that will work in a different database.

Example:

Here is the same search criteria (diabetes + self management), executed with database-specific search queries for three different databases:

CINAHL

(diabetes OR diabetic* OR (MH "Diabetes Mellitus+"))
AND
(“self management” OR “self care” OR “self monitoring” OR “self regulation” OR (MH "Self-Management") OR (MH "Self Care+"))

PubMed

(“diabetes”[tiab] OR “diabetic*”[tiab] OR "Diabetes Mellitus"[Mesh])
AND
(“self management”[tiab] OR “self care”[tiab] OR “self monitoring”[tiab] OR “self regulation”[tiab] OR "Self-Management"[Mesh])

Web of Science

(diabetes OR diabetic*)
AND
(“self management” OR “self care” OR “self monitoring” OR “self regulation”)

Resources for Translating Queries Between Databases