Use the included links to explore each of these steps in more detail.
For additional help, check out Searching School, a YouTube series created by a librarian to help researchers learn how to develop structured searches:
With a review question in mind, consider what variables you would like to build the database search around. For comprehensive searches, it is typically to only include 2-3 major concepts/variables in the search. For example, consider the review question:
What are the factors related to teacher preparation that influence the development of teaching competence among novice nurse educators?
From this review question 2 core concepts (the population and outcome of interest) can be identified to build the search criteria:
| Concept 1: nurse educators | Concept 2: teaching competence |
|---|
A 2 concept search like this will be structured in the database to ask the database to only retrieve records that meet these two search criteria (i.e., the records indicate something about nurse educators, and something about teaching competence)
With the core search concepts in mind, you can begin to identify keywords that describe these concepts. After doing some initial scoping searches, 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.
| Search Terms | Concept 1: nurse educators | Concept 2: teaching competence |
|---|---|---|
| Keywords |
nurse educator nurse teacher nurse instructor nursing faculty nurse lecturer nurse tutor |
teaching competence teaching competency instruction skill teaching skills teaching ability teaching effectiveness |
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.
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:
These are 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 evaluate the relationship that exists.
These are value-laden words 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.
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)
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.
Adapted from: VanLeer, L. (n.d.). Academic Guides: Keyword Searching: Finding Articles on Your Topic: Select Keywords. Retrieved September 30, 2022, from https://academicguides.waldenu.edu/library/keyword/search-strategy
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, the record for an article about nursing professors would be tagged with the MeSH term “Faculty, Nursing”. CINAHL uses a similar, but separate thesaurus of subject headings called the CINAHL Subject Headings.
In order to identify the subject headings that describe your concepts of interest:
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 (using a field tag) may be a more targeted way to search. A subject field search will only return results where the search term appears as the subject of that article. Consider the following example from PubMed, where the [MeSH] field tag is used:
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.
| Search Terms | Concept 1: nurse educators | Concept 2: teaching competence |
|---|---|---|
| Keywords |
nurse educator nurse teacher nurse instructor nursing faculty nurse lecturer nurse tutor |
teaching competence teaching competency instruction skill teaching skills teaching ability teaching effectiveness |
|
MeSH Terms (subject headings in PubMed) |
"Faculty, Nursing"[MeSH] | "Professional Competence"[Mesh] |
| CINAHL Subject Headings | MH "Faculty, Nursing" | MH "Professional Competence" |
Once you have identified important keywords and subject headings that describe your core concepts, you can use Boolean operators, punctuation, and and field tags to construct a query in the database. All these elements can be combined into one long search string, or used as part of a 'building block' style search, where you run separate searches for each concept of interest, and then combined each concept set together.
A complex search can be built by putting search terms, Boolean operators, punctuation, and field tags together all together into one long query string that gets entered into the database. 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.
If you're building a more complex search, it can be helpful to structure it as 'building blocks' where you run a separate search for each core concept, and then ask the database to combine those concept blocks with AND.
| Search Number | Search Query | Results |
|---|---|---|
| S3 | S1 AND S2 | 599 |
| S2 | ( (MH "Professional Competence") OR teach* skill* OR teach* competenc* OR teach* effective* OR teach* abilit*) | 32,457 |
| S1 | ( (MH "Faculty, Nursing") OR "nursing faculty" OR "nurse educator*" OR "nurse instructor*" OR "nurse teacher*" OR "nurse lecturer*" OR "nurse tutor") | 22,903 |
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 core search concepts, or perhaps 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: |
|---|---|
| educator | 49,000 |
| educator AND nursing | 14,000 |
| educator AND nursing AND preparation | 756 |
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: |
|---|---|
| educator | 49,000 |
| educator OR instructor | 55,000 |
| educator OR instructor OR professor | 70,000 |
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 educators, a search for that exact word will only return records with the word educators. But searching for educat* will be much broader, capturing records that contain the words education OR educate OR educator OR educators etc.
| Search Query: | Number of Results Returned: |
| educators | 49,000 |
| educat* | 850,000 |
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: |
| nurse educator | 7,500 |
| "nurse educator" | 1,200 |
Different database platforms may allow for different phrase searching functions. See these resources for the specifics related to common platforms:
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.
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:
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:
The art of crafting a database search requires finding a balance between sensitivity and specificity, given the purposes of the search.
Because the purpose of evidence synthesis projects, like integrative reviews, is to synthesize all the available evidence on a topic, searchers are encouraged to prioritize sensitive for the database searches, and rely on the manually screening process to narrow the pool of results.
A sensitive search can be built by integrating a flexible set of keywords and subject headings, and minimizing the database filters that are applied.
In capturing most of the relevant literature, there is a low risk that a sensitive search omits relevant literature
By design, a sensitive search will also capture a large number of ‘false hits’, so more time must be spend manually screening through search results to identify truly relevant results
See the section below on Broadening a Search for methods to make a database search more sensitive.
Specific searches are often built by using more specific search terms, phrase searching, and database limits.
Specific searches will capture some portion of the existing relevant literature, and by design, the results will contain fewer irrelevant records (“false hits”), leading to less time spent manually screening through records.
A search that prioritizes specificity holds a greater potential for missing relevant literature.
See the section below on Narrowing a Search for methods to make a database search more specific.
If you need to broaden your search (make the search more "sensitive"), there are a a few strategies that may help expand your search.
Survey your initial search results to identify any additional keywords or synonyms that might broaden the search. Consider acronyms, spelling variants and alternative phrases and integrate them into the search with the Boolean operator OR.
| Search Query | Results |
| contraception AND Obamacare | 91 |
| contraception AND (Obamacare OR "Affordable Care Act" OR ACA OR "Patient Protection and Affordable Care Act" OR PPACA) | 219 |
"Exploding" a subject heading means asking the database to retrieve articles with that subject heading, as well as articles that have been tagged with terms that are under that term in the subject heading tree. For instance, in CINAHL, exploding the term "Telehealth" would include narrower terms like "Telemedicine" and "Telenursing".
| Search Query | Results |
| (MH "Telehealth") | 9,500 |
| (MH "Telehealth+") | 26,000 |
PubMed automatically explodes MeSH terms to include narrower subject headings.
Also see:
Truncation symbols in a database (such as "*" or "?") allow you to search on a root word and include plurals or variants.
For example:
Be aware that truncation may also retrieve false hits. For instance, a search on nurs* would also retrieve “nursery school”. Using a shorter root word will likely map to more variants, while using a longer root word will be more specific.
Remember that each search criteria that is included with AND will restrict the number of results (i.e.,, a search that is built around 3 criteria will typically be more narrow than a search that is built using just 2 of those concepts). So if your search retrieves too few results, or a narrow range of results, it may be necessary to evaluate the number of concepts you're connecting with AND. For instance, you could remove the 'outcome' concept from the search criteria, and use the outcome of interest as a manual screening criteria instead.
| Search Query: | Number of Results Returned: |
|---|---|
| type II diabetes AND telehealth AND blood glucose | 195 |
| type II diabetes AND telehealth | 556 |
For many healthcare-related questions, you may be primarily interested in the most recent information, so it is common to apply filters to only show results from the last 5 or 10 years.
If your patient population has a particular age designation, many of the health sciences databases allow you to apply a filter for the age group that was studied in the article
Many databases allow you to limit a search to only peer-reviewed articles, or articles that were published in scholarly journals.
(For more information about how to determine if an article is peer-reviewed, see "Limiting to 'Peer Reviewed' Articles" below)
In addition to having a filter for 'peer reviewed articles', CINAHL has a filter that allows you to limit to only 'Research Articles', which are articles that report research study or examination of subject matter that uses investigational or experimental techniques (i.e. includes data collection, subject selection, methodology and discussion of results)
Some databases allow you to filter by publication type or study methodology (e.g., case studies, editorials, news articles, as well as higher levels of evidence such as clinical trials, systematic reviews, and meta-analyses). Limiting by publication type is one way to ensure that the research retrieved was based on a more rigorous methodology and thus reflects a higher level of evidence.
PubMed and CINAHL have filters for "Clinical Queries" - pre-formulated search strategies ("hedges") that can be applied to your search to retrieve only clinically sound studies.
For additional support related to applying filters in the major databases, see this set of tutorial videos.
If an initial search retrieves too many results, it may be necessary to integrate an additional concept, using the Boolean operator AND. For example, if you are searching based on a PICO question, you may consider integrating the O (outcome of interest) into the search strategy.
| Search Query: | Number of Results Returned: |
|---|---|
| type II diabetes AND telehealth | 556 |
| type II diabetes AND telehealth AND blood glucose | 195 |
Subject headings are often hierarchical, with broader headings encompassing more specific terms. For instance, while a search with the MeSH term "Diabetes Mellitus" may be too broad, you could try a narrower term like "Diabetes, Gestational".
Some databases, like CINAHL and PubMed, allow you to append sub-headings to the subject terms, allowing for a narrower search. For instance, if you were interested in the use of insulin, but only in the context of its use as a treatment, you could apply the sub-heading "therapeutic use" : "Insulin / therapeutic use"
Also see:
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.
Here is the same search criteria (diabetes + self management), executed with database-specific search queries for three different databases:
(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+"))
(“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])
(diabetes OR diabetic*)
AND
(“self management” OR “self care” OR “self monitoring” OR “self regulation”)
