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Health (Nursing, Medicine, Allied Health)

Guide to research sources and tools for locating health evidence in books, journals, databases.

Definitions of Important Terms Related to Database Searching

Glossary A-Z


Literature Databases

also: article databases, research databases

Searchable, electronic collections of records that describe published literature  

Databases are searchable collections of descriptive records, typically created from the indexing of scholarly journal articles.

Literature databases are typically composed of records that describe articles that were published in scholarly journals, trade journals, newspapers and magazines, as well as resources like conference proceedings and papers, reports, and government or legal publications.  

Databases may be multidisciplinary/multipurpose, or have a specific focus that determines which sources get indexed into records for the database, based on subject focus, date coverage, and material type.  For example, see the table below for scope information related to some major health sciences databases.

Some databases may be entirely bibliographic, meaning they contain only records that describe literature.  Other databases are considered full-text, which means that in addition to bibliographic descriptions, they contain the entire texts of the associated document.  Other databases may contain full text content for a subset of their records. 

Databases are typically made available electronically on a commercial basis by public or private database producers (sometimes known as vendors), who provide a platform or interface to access and search the database.

Descriptions of 2 Major Health Databases

Database

MEDLINE

CINAHL

Platform/Vendors

>Ovid (Wolters Kluwer)

>PubMed (NLM)

>EBSCO

Subject Coverage

biomedicine and health, broadly, including:

  • life sciences
  • behavioral sciences
  • chemical sciences
  • clinical care
  • public health, health policy development
  • health-related education
  • aspects of biology, environmental science, marine biology, plant and animal science, biophysics & chemistry

Nursing & allied health, broadly, including: 

  • healthcare and biomedicine
  • nursing profession & nursing disciplines
  • public/community health
  • consumer health
  • occupational therapy
  • physical therapy
  • speech-language pathology

Publication Type Coverage

  • Mostly scholarly journals
  • Small number of newspapers, magazines, and newsletters 
  • Academic journals
  • Trade publications newspapers, magazines, and newsletters 
  • Nursing dissertations/theses
  • Educational material (Evidence Based Care Sheets, Quick Lessons, CE Modules, Patient Education Handouts)

Full Text Access

No; records may contain link outs to full text on PubMed Central or publishers’ pages

Partial; some journals are held in full text in CINAHL

References
  1. International Encyclopedia of Information and Library Science, edited by John Feather, and Paul Sturges, Taylor & Francis Group, 2003. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/nyulibrary-ebooks/detail.action?docID=178969.

  2. Medline overview. (n.d.). [Product, Program, and Project Descriptions]. Retrieved September 27, 2022, from https://www.nlm.nih.gov/medline/medline_overview.html 


Database Platform

The interface through which a database is accessible and searchable 

Database platforms may be provided by:

  • commercial database vendors (e.g., ProQuest or EBSCO, or Ovid (Wolters Kluwer))
  • non-profit or professional organizations (e.g., PsycNET, from the American Psychological Association)
  • public, government entities (e.g., PubMed, from the US National Library of Medicine)

Bibliographic Record

also: database record, descriptive record, reference, citation

An entry in a database which represents and describes a specific resource. 

A bibliographic record contains the data elements necessary to help users identify and retrieve that resource, as well as additional supporting information, presented in a formalized bibliographic format. Bibliographic records are composed of metadata fields, fields that contain information about the resource that aids in discovery and organization (e.g., title, abstract, author, publication details, resource type, and subject headings).

References
  1. Bibliographic record. (2022). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Bibliographic_record&oldid=1100147395 


Metadata

Data that provides information about other data (“data about data” or “information about information”)

In bibliographic records, important descriptive metadata includes title, author, source type, and subject descriptors.

References
  1. Merriam-Webster. (n.d.). Metadata. In Merriam-Webster.com dictionary. Retrieved September 27, 2022, from https://www.merriam-webster.com/dictionary/metadata 


Keywords

also: free text terms

Terms that describe a topic of interest

Keywords are flexible and variable, often drawn from a researcher’s natural language descriptions of the core concepts of interest.  Keywords can be used as search terms in a database interface and used to ask the database to retrieve records that contain those terms.  Ideally, keywords should be significant, meaningful terms that describe or relate to a concept of interest.  

Because databases generally search for the exact terms that are entered, certain types of keywords should be avoided, including relationship words (e.g., cause, contrast, correlation), and judgment words (e.g., advantage, disadvantage, benefit). For more guidance, see Crafting a Search.

Keywords can be searched for in specific fields of a database record (e.g., title or abstract) using field tags or in an unqualified search, where the search will likely retrieve records that contain the keyword in any field of the database record.  


Subject Headings

also: index terms, controlled subject vocabulary

Terms or phrases that are selected to describe the conceptual content of an information resource (i.e., what a text is about)

When a record gets created for a database, subject headings are included in order to better describe the information resource.  Those subject headings are usually drawn from standardized lists (i.e., thesauri) that establish definitions and preferred usages.  

Subject headings are typically based on a controlled vocabulary, meaning that for any given concept, only one term is used (e.g., articles about “cars”, or “motor vehicles” or “sedans” would all be tagged with the same subject heading: “automobiles”).  Subject headings may differ from the natural language text words or keywords that are used to describe a concept.

Subject headings are also typically hierarchical, or exist within a tree structure; narrower, more specific subject headings sit under broader, more general subject terms.  For example, in the MeSH hierarchy, the term “Mass Vaccination” is a more specific term under “Vaccination”, which itself is a more specific term under “Immunization”

Tree View for MeSH Term “Vaccination”:

"Tree view" showing the hierarchy of subject terms in the MeSH thesaurus. The hierarchy is: "All MeSH Categories" > "Health Care Category" > "Environment and Public Health" > "Public Health" > "Public Health Practice" > "Primary Prevention" > "Immunization" > "Vaccination" > "Mass Vaccination."

Different databases employ different thesauri of subject headings in the indexing process.  For example,

  • records in MEDLINE are indexed with Medical Subject Headings (MeSH Terms)
  • records in CINAHL are indexed with CINAHL Subject Headings
  • records in PsycINFO are indexed with APA Index Terms

Field tags can be used to conduct “subject searching”, where only records with specific headings in the subject field are retrieved.  

For more information, see Crafting a Search-Identifying Subject Headings


Explosion

also: “exploding a heading”

Directing the database to search for a requested subject heading as well as any more specific terms that occur under that heading in the subject hierarchy (tree)

By default, when searching PubMed using MeSH terms, the terms will be searched with ‘autoexplode’, and the search will automatically include records tagged with the more specific headings underneath that term.  For instance, a search for Vaccination[MeSH] will automatically also retrieve records tagged with the MeSH term “Mass Vaccination”, because that term is under “Vaccination” in the subject tree

In other databases, subject explosion does not happen automatically; the user must specify explosion, if needed, using specific search syntax.  For instance, CINAHL uses the + sign to denote explosion (e.g., MH "Vaccines+"), whereas databases on the Ovid platform (EMBASE, MEDLINE, PsycINFO) add exp to the subject term (e.g. exp vaccine/).

For more information see: Strategies for Broadening a Search - 2. Explode Your Subject Headings


Subheadings

also: qualifiers

Pre-specified terms that are used with main subject headings to refine the meaning of a main heading by specifying one of its aspects

For example, the MeSH thesaurus contains qualifiers that can be used with MeSH terms for conditions, drugs, organs, and other subjects (e.g., “Breast Neoplasms/genetics” for the genetics of breast cancer, or “Uterus/surgery” for surgery performed on the uterus).  CINAHL subject headings can also be made more specific with available subheadings.  


Boolean Operators

also: Boolean connectors

Short commands that are used to connect database search terms to broaden or narrow a search
AND

Connecting search terms with AND tells the database to retrieve records only if each of those terms appear in the record.  The operator AND is typically used to connect conceptually distinct terms.  The more concepts that are connected with AND, the narrower the search results become.  

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 records from the search result if the record contains the specified term.  Adding an excluded term with NOT will create a narrower, more specific search. 

A word of caution: the NOT operator should be used sparingly and carefully; because the database will simply exclude records in which the specified word appears, it is possible to accidentally exclude relevant records that happen to contain that term.

For more information about Boolean operators and examples, see: Crafting a Search-Employing Boolean Operators


Proximity Search

also: adjacency search

A search query that retrieves records if two or more of the specified terms are located within a specific distance (number of words or characters) of each other in the text

For example, in the EBSCO platform the following query will retrieve records if the word “gender” is found within 3 words of the word “bias”.

gender N3 bias

Proximity searching is useful in cases where there are multi-word variations of a keyword that might be relevant to include.  For instance, the example above will locate records if they contain the phrase “bias based on gender”, “gender related bias”, “bias due to gender”, etc.

Different databases platforms use different operators for adjacency searching, as well as different rules for counting the number of intervening words.  

Platform-Specific Information for Proximity Searches

Phrase Searching

Using specific punctuation or commands to look for exact phrases

Many databases use quotation marks as the punctuation for phrase searching.  For instance, searching “metabolic syndrome” will only retrieve records if that exact phrase is used in the record (i.e., the results won’t contain records if that specific phrase is not used in the record).

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


Truncation

Also: wildcard

Specific punctuation used in database search queries to account for unknown characters, multiple spellings or variant endings.

Many databases use an asterisk * as the wildcard punctuation.  For example, the query allerg* would retrieve records containing the word allergy, or allergies, or allergic, or allergist, or allergen, etc

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


Field Tag

also: field code, search tag

A short code (often two letters) that can be appended to search terms to specify in which field of the record the database should try to locate that term 

For example, the field code TI can be used in CINAHL to specify a phrase should be found in the title field: TI “environmental racism”

For more information, see Crafting a Search - Field Tags


Unqualified Search

A search in which a search field is not specified, so the database defaults 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:


Search Query

also: search string(s)

The string of terms, punctuation, Boolean operators and field tags that are entered into the database in order to retrieve records

Search queries can range from relatively simple to more complex.  Simple search queries often rely on keyword searches, and allow the database to run the search based on default settings.  Complex search queries might include more operators, specific punctuation, database-specific field tags, and may make use of a ‘building block’ style of searching.  

A example of a simple search query:

misinformation AND nutrition

A more complex search query (PubMed):

("Disinformation"[Mesh] OR "Communication"[Mesh:NoExp] OR "Information Literacy"[Mesh] OR misinformation[TIAB] OR "fake news"[TIAB] OR infodemic[TIAB]) AND ("Nutritional Sciences"[Mesh] OR "Nutrition Therapy"[Mesh] OR "Diet Therapy"[Mesh] OR "Food"[Mesh] OR "Dietary Supplements"[Mesh] OR nutrition[TIAB] OR food[TIAB] OR diet[TIAB]) 


“Building Block" Style Search

A style of building a database search in which the user builds queries (“blocks”) that each core topic separately, and then combines the blocks or search queries together.

A building block style search can often be created using the tools within a database platforms’ advanced search or search history.

Example  ‘building block’ search:
An example of a 'building block' style search based on 3 concepts (nutrition + misinformation + conspiracy)

Search Number

Search Query

Results

#4

#1 AND #2 AND #3

31

#3

conspirac* OR conspiratorial OR distrust* authorit*

1,680

#2

misinformation OR disinformation OR “fake news” OR infodemic

677,444

#1

nutrition* OR food OR diet* OR supplement*

2,522,212

The same search, run as a single query:

(nutrition* OR food OR diet* OR supplement*) AND (misinformation OR disinformation OR “fake news” OR infodemic) AND (conspirac* OR conspiratorial OR distrust* authorit*)


Database Filters

also: database limits or limiters

Search tools within a database interface that allow the user to restrict their search results to only include records that contain specific metadata

For example, a publication date limiter will tell the database to only retrieve records if the publication date listed falls within a specified range.  

Different databases offer different filtering capabilities, depending on the metadata that is present in the database’s records. For instance, because health-focused databases (like MEDLINE, CINAHL and PsycINFO) include metadata that describe the characteristics of the participants in a study, it is possible to limit a search by elements like participants; age, sex, or species.


False Hits

Records that appear in database search results because they technically meet the search criteria, but are actually conceptually irrelevant  

For instance, the search query burns AND dressing will retrieve records about dressing burn wounds. But the results set might also contain records for articles about dressing as an activity of daily living, by author Robert Burns.


Sensitive Search

also: broad search

A database search that is designed to capture most of the existing relevant literature on a topic

A sensitive search can be built by integrating a flexible set of keywords and subject headings, and minimizing the database filters that are applied.

Advantage:

In capturing most of the relevant literature, there is a low risk that a sensitive search omits relevant literature 

Disadvantage:

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 

Database searches that are performed for evidence synthesis projects (e.g., systematic reviews, scoping reviews, integrative reviews), should err on the side of sensitivity.


Specific Search

also: narrow search

A database search that is designed to only retrieve highly relevant records. 

Specific searches are often built by using more specific search terms, phrase searching, and database limits.

Advantage

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. 

Disadvantage

A search that prioritizes specificity holds a greater potential for missing relevant literature.


Replicable Search

also: reproducible search

The record of a database search query that contains sufficient details as to allow it to be replicated by another search

Such details include: 

  • exact terms, punctuation, Boolean operators, and field tags used in the search query
  • database platform that used
  • date search was executed
  • details of all  database limiters (or expanders) that were applied to the search 

To save a reproducible search string, it may be useful to create a database account and save the exact search that was run, in the native database environment.  For more information, see: Saving Searches.


Snowballing

also: citation pearl growing

Using a relevant source to lead to more sources on a topic

Strategies for snowballing based on a relevant source include:

  • Locating a record for the source in a database like PubMed, ProQuest Central, Scopus, or Web of Science, and looking for sections/links like “you may also like…” or "related articles” or “find similar results"
  • Mining the source’s reference list and following up on entries that may be relevant (review articles, including systematic reviews, can be a goldmine of related literature)
  • Performing “cited reference searching” to track additional articles that have cited a particular, relevant source

Handsearching

also: hand search, hand-search

A manual method of scanning select journals from cover to cover, page-for-page for relevant articles in case they were missed during indexing

Handsearching is a process employed in systematic literature searches that is meant to complement structured searching of electronic databases in order to identify any additional eligible sources that were not located by the database search.    Handsearching may also involve ‘snowballing’ searches, where the reference lists and citations of included studies are checked for additional sources.  

For more information, see Rutgers’ LibGuide - Hand Searching 


Cited Reference Searching

also: citation searching, citation chasing

Using an online tool or citation index to determine what other works have cited a given text

Several online tools and citation indexes allow the user to track the citation of a given publication (forward in time).  In other words, these tools will generate a list of references that have cited a given text since it was published.  

For more information, see: Guide to Cited Reference Searching


PRISMA Diagram

also: PRISMA flowchart, search flow diagram

A diagram that visually depicts the flow of records or studies through the screening phases of a systematic literature review

PRISMA diagrams map out the number of records identified, included and excluded, and the reasons for exclusions.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a set of reporting standards designed to ensure transparent reporting of systematic reviews.  PRISMA diagrams are most appropriately used in conjunction with the PRISMA Checklist, a 27-item checklist addressing the introduction, methods, results and discussion sections of a systematic review report.

Creation of a PRISMA diagram requires careful record keeping during the screening phase of an evidence synthesis project, which requires the use of citation management software (e.g., Zotero or EndNote), and/or systematic review screening software (e.g., Covidence).


For more information, see: Tutorials & Tools for Literature Reviews