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Challenges in Multidisciplinary Systematic Reviewing: A Study on Social Exclusion and Mental Health Policy
Author(s) -
Curran Claire,
Burchardt Tania,
Knapp Martin,
McDaid David,
Li Bingqin
Publication year - 2007
Publication title -
social policy and administration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 63
eISSN - 1467-9515
pISSN - 0144-5596
DOI - 10.1111/j.1467-9515.2007.00553.x
Subject(s) - multidisciplinary approach , systematic review , management science , discipline , engineering ethics , mental health , social exclusion , data science , psychology , sociology , computer science , social science , medline , political science , engineering , psychotherapist , law
In the clinical sciences, systematic reviews have proved useful in the aggregation of diverse sources of evidence. They identify, characterize and summate evidence, but these methodologies have not always proved suitable for the social sciences. We discuss some of the practical problems faced by researchers undertaking reviews of complex and cross‐disciplinary topics, using the example of mental health and social exclusion. The barriers to carrying out social science and cross‐disciplinary reviews are reported and some proposals for overcoming these barriers are made, not all of them tried and tested, and some of them controversial. Using a mapping approach, a wide‐ranging search of both clinical and social science databases was undertaken and a large volume of references was identified and characterized. Population sampling techniques were used to manage these references. The challenges encountered include: inconsistent definitions of social phenomena, differing use of key concepts across research fields and practical problems relating to database compatibility and computer processing power. The challenges and opportunities for social scientists or multidisciplinary research teams carrying out reviews are discussed. Literature mapping and systematic reviews are useful tools but methods need to be tailored to optimize their usefulness in the social sciences.