Search syntax refers to the rules and conventions used to construct search queries within a database or search engine. It involves the use of specific symbols, operators, and formatting techniques to refine and structure your search criteria. This includes the use of Boolean operators (such as AND, OR, and NOT), field codes, truncation symbols, and proximity operators to combine keywords, search terms, and even search lines into queries that the database can interpret and execute.

It’s important to note that search syntax is not universal - it can vary significantly between databases. Each platform may have its own set of accepted commands, operators, and field tags. Always refer to the database’s help documentation or search guide to ensure your syntax aligns with that system’s requirements.

If a search query does not run or produces an error, the issue usually lies in the construction of the query rather than the database itself. This often indicates a mismatch in syntax or the use of unsupported commands. Reviewing your query carefully - and adjusting your terms or formatting - is an essential part of refining and improving your search.

At its core, every search query is constructed using Boolean logic (AND, OR, NOT) to specify the relationships between different terms and search lines. 

Virtually every search engine, whether it's a web search engine like Google or a database search engine like CINAHL or Scopus, operates based on Boolean logic principles. By using Boolean operators such as AND, OR, and NOT, users can refine their search queries to retrieve more relevant and targeted results.

This universality of Boolean logic in search engines allows you to apply similar search strategies across different platforms, making it easier to navigate and retrieve information from various sources. For example, if you have written your primary search in CINAHL, with adjustment and proper adaptation, it will run in almost any other database you choose to search.

1. Boolean Operators:
   • AND: When you use 'AND' between two search terms, it tells the search engine to retrieve results that include both terms. For example, if you search for 'Bruce Springsteen AND albums,' you'll get results that mention both Bruce Springsteen and albums.
   • OR: When you use 'OR' between two search terms, it tells the search engine to retrieve results that include either of the terms. For example, if you search for 'Bruce Springsteen OR "The Boss",' you'll get results that mention either Bruce Springsteen or The Boss.
   • NOT: When you use 'NOT' before a search term, it tells the search engine to exclude results that include that term. For example, if you search for 'Bruce Springsteen NOT Born in the USA,' you'll get results that mention Bruce Springsteen but exclude any results related to the album 'Born in the USA.'
2. Combining Operators:
   • You can combine multiple operators to create more complex search queries. Let's say you're interested in finding information about Bruce Springsteen's albums, but you want to specifically exclude results related to live performances. Here's how you could construct the search query: 'Bruce Springsteen AND (albums OR studio).'
3. Parentheses (Nesting):
   • Parentheses can be used to group terms and control the order of operations. For example, if you want to search for songs by Bruce Springsteen but also include songs by his band, you might search for '((Bruce Springsteen OR E Street Band) AND (song* OR "Atlantic City")).'

By understanding and using these basic principles of Boolean logic, you can create more targeted and effective searches to find information.

1. Wildcards (also known as Truncation): Wildcards like asterisks (*) or question marks (?) enable you to search for variations or partial matches of keywords. For instance (operation of Wildcards will vary from one database provider to another, so check before you begin your search):
   • 'lyric*' would retrieve results containing 'lyric,' 'lyrics,' 'lyrical,' 'lyricism,' etc.
   • 'wom?n' would retrieve results containing 'woman' or 'women.'
2. Phrase Searching: Quotation marks (" ") indicate that words should be searched together as a phrase. This helps retrieve results where the words appear together in the specified order. By enclosing a phrase within quotation marks in a search query, you instruct the search engine to look for the precise phrase as it appears within the text:
   • "Born in the USA" the database's search engine will look for documents or records containing this exact phrase within the selected fields (title, abstract, etc).
3. Proximity Operators: Proximity operators like 'NEAR', 'adj', or 'WITHIN' allow you to specify the distance between keywords in your search. For example, 'child NEAR/3 education' would retrieve results where 'child' and 'education' appear within three words of each other. They imply a more semantic relationship between the concepts being searched for. For example, using a proximity operator can indicate that the terms should appear close to each other in the document, suggesting a stronger association between them. This helps narrow down search results to documents where the terms are contextually related or appear near each other. It's important to note that not all databases support proximity operators.

Keywords and Free-Text Searching

Keyword searching uses the natural language words and phrases that appear in the title, abstract, or full text of a record. Unlike controlled vocabularies, keywords are not standardised - authors choose their own terminology, which means there can be variation in how topics are described (e.g. "teenagers" vs. "adolescents").

Keywords are useful when:

• You're searching emerging or interdisciplinary topics that may not yet be well indexed

• You're looking for specific phrases, jargon, or terminology used by authors

• You want to capture a broader or more flexible range of language

In many databases, keyword searches are run in the title (TI), abstract (AB), or full-text fields. You can also use tools like truncation (e.g. educat* for education, educator, educational) or phrase searching (using "quotation marks") to improve precision.

While keyword searching is powerful, it’s also more prone to inconsistency - so it works best when combined with controlled vocabulary terms where available. Understanding how a database handles keywords and indexing will help you decide how best to build your strategy.

Controlled vocabulary searches

Controlled vocabularies are standardised sets of terms that are used to describe a particular topic or subject in databases. They are often subject specific, consist of preferred terms and associated synonyms or non-preferred terms, and are designed to capture the unique terminology and concepts within a particular field or domain. In MEDLINE EBSCO, the field is MH Exact Subject Heading. When a record is added to a database, it may be assigned one or more terms to describe its subject matter, this makes it easier to find when searching for records about a specific topic. 

Every record is assigned one or more of these standardised terms which makes it easier to search for records on a particular topic because all records that are about the same topic will use the same terms.  Some databases rely more on keyword searching, where users can input free-text terms to search for relevant content. It's important to be aware of the indexing and search capabilities of the specific databases you are using and adapt your search strategies accordingly.

When crafting a complex search that involves various sets of terms related to specific aspects of your research question, consider saving each component as a separate search (referred to as 'blocks'). These individual searches can then be run independently or combined with other searches as needed.

Boolean logic serves as the fundamental building blocks of search queries. In this case, we are addressing the following research question: 'How does physical fitness and exercise impact stroke recovery, as evidenced by randomised controlled trials (RCTs)?'. We will be looking for keywords and terms for 'stroke', another for physical fitness interventions, and a subsequent collection to identify randomised controlled trials. We want to retrieve documents which contain all these criteria.

Think of it like a Venn diagram; each set of terms functions as a collection of keywords combined using the OR function, expanding the search to include any of the terms within a set. The overlaps represent the AND operator, requiring all terms to be present simultaneously, thus leading to more precise and targeted information retrieval.

The above search contains three collections of terms, or blocks. In a triple intersection, each element or item must meet the criteria specified in all three sections of the query. Therefore, it contains at least one word from each of the sections, satisfying the conditions set forth in the query's criteria.

Using a combination of both free text/keywords and controlled vocabulary terms where available. This will maximise the retrieval of potentially relevant records. Let's use the National Library of Medicine's Medical Subject Headings (MeSH) in the MEDLINE EBSCO database in the following example.

When the query containing both controlled vocabulary and keywords is entered into a database, it might look as follows:

[Screenshot image of search strategy in in the EBSCO platform, displaying both keywords, controlled vocabulary and search operators.]

The final line integrates the terms from each preceding step into a single line which represents your query. Line-by-line searching involves breaking down the search process into smaller, more focused steps. This method helps researchers adjust their search terms based on initial findings, ensuring a thorough and targeted exploration of relevant literature.