Construct rigorous systematic search strategies for literature reviews
A skill for designing and executing comprehensive, reproducible literature search strategies for systematic reviews, scoping reviews, and meta-analyses. Follows PRISMA 2020 guidelines and Cochrane Handbook best practices.
Structure your research question using PICO (or variants):
P - Population / Problem: Who or what is being studied?
I - Intervention / Exposure: What is the treatment or exposure?
C - Comparison: What is the alternative?
O - Outcome: What is being measured?
Variants:
PICOS: adds Study design
SPIDER: Sample, Phenomenon of Interest, Design, Evaluation, Research type
PCC: Population, Concept, Context (for scoping reviews)
def pico_to_search_blocks(pico: dict) -> dict:
"""
Convert a PICO question into search concept blocks.
Args:
pico: Dict with keys 'population', 'intervention', 'comparison', 'outcome'
Each value is a list of synonyms/related terms
Returns:
Search blocks ready for Boolean combination
"""
blocks = {}
for component, terms in pico.items():
# Expand each term with common variants
expanded = []
for term in terms:
expanded.append(f'"{term}"')
# Add truncation variants
if len(term) > 5:
expanded.append(f'{term.rstrip("s")}*') # basic stemming
blocks[component] = expanded
# Build final query: AND between blocks, OR within blocks
query_parts = []
for component, terms in blocks.items():
block = ' OR '.join(terms)
query_parts.append(f'({block})')
final_query = ' AND '.join(query_parts)
return {
'blocks': blocks,
'combined_query': final_query,
'n_concepts': len(blocks)
}
# Example: RQ: "Does mindfulness meditation reduce anxiety in college students?"
pico = {
'population': ['college students', 'university students', 'undergraduate students',
'higher education students'],
'intervention': ['mindfulness', 'mindfulness meditation', 'mindfulness-based stress reduction',
'MBSR', 'mindfulness-based cognitive therapy', 'MBCT'],
'outcome': ['anxiety', 'anxiety disorder', 'generalized anxiety', 'test anxiety',
'anxiety symptoms', 'state anxiety', 'trait anxiety']
}
result = pico_to_search_blocks(pico)
print(result['combined_query'])
def adapt_search_for_database(base_query: str, database: str) -> str:
"""
Adapt a base search string for different database syntaxes.
"""
adaptations = {
'pubmed': {
'truncation': '*',
'phrase': '"..."',
'proximity': None, # PubMed doesn't support proximity
'field_tags': {'title': '[ti]', 'abstract': '[tiab]', 'mesh': '[MeSH]'},
'notes': 'Add MeSH terms for each concept block'
},
'web_of_science': {
'truncation': '*',
'phrase': '"..."',
'proximity': 'NEAR/N',
'field_tags': {'title': 'TI=', 'topic': 'TS=', 'author': 'AU='},
'notes': 'Use TS= for topic search (title+abstract+keywords)'
},
'scopus': {
'truncation': '*',
'phrase': '"..."',
'proximity': 'W/N',
'field_tags': {'title': 'TITLE()', 'title_abs': 'TITLE-ABS-KEY()', 'author': 'AUTH()'},
'notes': 'Use TITLE-ABS-KEY() for comprehensive searching'
},
'psycinfo': {
'truncation': '*',
'phrase': '"..."',
'proximity': 'Nn',
'field_tags': {'title': 'TI', 'abstract': 'AB', 'thesaurus': 'DE'},
'notes': 'Use DE field for PsycINFO thesaurus terms'
}
}
db = adaptations.get(database.lower(), {})
adapted = base_query # Start with base query
return {
'database': database,
'query': adapted,
'syntax_notes': db.get('notes', ''),
'truncation': db.get('truncation', '*'),
'field_tags': db.get('field_tags', {})
}
Document every search completely:
search_documentation:
date_searched: "2026-03-09"
databases:
- name: "PubMed/MEDLINE"
interface: "PubMed.gov"
date_coverage: "1966-present"
search_string: |
(("college students"[tiab] OR "university students"[tiab])
AND ("mindfulness"[tiab] OR "MBSR"[tiab])
AND ("anxiety"[tiab] OR "anxiety disorders"[MeSH]))
results_count: 342
filters_applied: "English language; 2010-2026"
- name: "Web of Science"
interface: "Clarivate"
date_coverage: "1900-present"
search_string: |
TS=("college student*" OR "university student*")
AND TS=(mindfulness OR MBSR OR MBCT)
AND TS=(anxiety)
results_count: 287
filters_applied: "Article or Review; English; 2010-2026"
grey_literature:
- "ProQuest Dissertations (N=45)"
- "Google Scholar first 200 results"
- "OpenGrey (N=12)"
- "Hand-searched reference lists of included studies"
total_before_dedup: 686
total_after_dedup: 493
deduplication_tool: "Covidence"
def prisma_flow(records: dict) -> str:
"""Generate PRISMA 2020 flow diagram data."""
flow = f"""
IDENTIFICATION
Records from databases: {records['from_databases']}
Records from other sources: {records['from_other']}
Duplicates removed: {records['duplicates']}
Records after dedup: {records['from_databases'] + records['from_other'] - records['duplicates']}
SCREENING
Title/abstract screened: {records['screened']}
Excluded at title/abstract: {records['excluded_screening']}
Full-text assessed: {records['fulltext_assessed']}
Excluded at full-text: {records['excluded_fulltext']}
Reasons: {records.get('exclusion_reasons', 'See table')}
INCLUDED
Studies in qualitative synthesis: {records['included_qualitative']}
Studies in meta-analysis: {records.get('included_meta', 'N/A')}
"""
return flow
After initial search execution:
Document every modification to the search strategy with rationale to maintain transparency and reproducibility.