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EBSCO settings that affect search results: Home

including "Boolean/Phrase" Search Mode, and "Find all my search terms"

Health Sciences Librarian

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John Wiswell

Levine Hall 542F & Belk Library 225, (828)262-7853

EBSCO settings that affect search results, including Boolean/Phrase Search Mode, and Find all my search terms

August 2023 default settings

APPsearch (EDS, EBSCO Discovery System)

Find all my search terms (not Boolean/Phrase)

Also search within the full text of articles.

Other EBSCO databases (except Music databases)

Find all my search terms (not Boolean/Phrase)

NOT Also search within the full text of articles.  Searching only titles/abstracts/headings/keywords.

Music databases


Searching only titles/abstracts/headings/keywords.  NOT Also search within the full text of articles.


What do these different Search Modes do?

Find all my search terms works pretty much like a Google search string.  EBSCO applies the AND function to the words.  Users may explicitly add Boolean operators, but AND is assumed.  Probably this search mode works best (most of the time) searching abstracts, titles, keywords, and subject headings.  Not full text of articles.

Although this search mode does not use proximity of search words, as described below, relevance ranking of results will rank results with search terms near one another higher, and those will be more likely to be seen.

Boolean/Phrase works better in some ways with searching in full text, plus it sometimes reads natural language strings better.  EBSCO looks for Boolean words and treats words on either side of them as rough phrases.  It looks for results that have those words near each other.  (It uses the NEAR5 or N5 function.)

Example: A student types in greenhouse gas emissions and climate change.  EBSCO sees the Boolean and.  It then looks for all results that have greenhouse within 5 words of gas, gas within 5 words of emissions, and climate within 5 words of change.

In this Search Mode, a space between words is not interpreted as AND.  Instead it triggers the much more strict NEAR5 proximity search.  I don't think it will count a word in the title as near a word in the abstract or subject headings.  (Or in abstract as near keywords, and so on.)

By the way, try the SmartText Searching sometime, maybe with a favorite article's abstract.  Unless you click the button, SmartText Search kicks in automatically in only one case, a search in APPsearch that yields no results. 

Also, be careful.  If you do an APPsearch search that yields no results, it will warn you that it's giving you SmartText Search results instead.  But if you then click the peer-reviewed articles button or limit the dates, that message will disappear.

Implications for setting defaults and for instruction

Do we want to follow Google and other fairly standard practices?  Or follow EBSCO's?

Should we aim to be consistent in instruction and examples?  For example, should we consistently model an example like

greenhouse gas emissions and climate change


"greenhouse gas" emissions "climate change"

Which settings will work best for freshmen?  Which for students about to graduate?


July 2023 -- The EBSCO databases, besides APPsearch, CINAHL, and SPORTDiscus, are set up to use Boolean/Phrase search mode and not Also search within the full text of articles.  I'm not finding any discussions in the literature (or social media), but this seems like the worst default combination of these two settings.  The proximity functions of Boolean/Phrase search mode are useful in searches of full text, whereas NEAR5 proximity is not very meaningful when applied to relatively small sets of words in abstracts/titles/keywords/headings.

How does the library decide on settings? How does it communicate about them?

Summer 2023 -- The E-Resources team held an open meeting (with cookies) and communicated through emails before and after the event.  They shared details about search functioning through emails and some informal conversations.

Do Apply equivalent subjects or Apply related words EVER have any effect?

I don't think so.

Relevance Ranking

Whichever Search Mode you use, APPsearch will do relevance ranking.  It's probably the same algorithm for any of the search modes, and proximity of search terms is probably an important factor.  So whether you use the proximity searching of Boolean/Phrase search mode or not, you will still get results with your search words near each other higher up your results page than they would have been otherwise.

Proximity searching is useful.


"Cultural humility" works best for explicit use of the term, which is often important, but cultural N5 humility might work better for the broader concept and catches text like this: cultural competence and humility.

Since we're thinking about natural language and large corpuses of full text -- AI and secondary research

Also, as you think about using ChatGPT and other popular AI applications, consider using questions like these:

Suggest a systematic search strategy to find published articles concerning interventions for autism.  The outcome should be related to change in initiating communication.

Where should I publish my article concerning measuring outcomes from interventions for autism?  I need a list of several peer-reviewed journals, with medium to high prestige, and zero or very low cost for authors.