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Multi-source synthesis of data to inform health policy

Published online by Cambridge University Press:  08 July 2011

Vibe Hjelholt Pedersen
King's College London
Pierre Dagenais
University of Montreal and Institut National d'Excellence en Santé et en Services Sociaux (INESSS)
Pascale Lehoux
University of Montreal


Objectives: To propose a new method for comparing and integrating original qualitative data with systematic reviews of quantitative and qualitative studies, demonstrated by a study of the psychosocial needs of chronic fatigue syndrome (CFS) sufferers in Québec.

Methods: A systematic literature review was performed across various databases for English and French language studies, on the psychosocial aspects of CFS. Qualitative, quantitative, and mixed method studies published between January 1994 and July 2008 were included. Unpublished literature and reference lists of included studies were also searched. Themes identified in the literature were used to guide semi-structured interviews with seventeen CFS-sufferers, mostly recruited from a large specialist practice in Montreal. Interviews were transcribed verbatim and validated by a research assistant. Transcripts were coded using the identified themes. New codes were created when new issues arose. All themes were subsequently synthesized into overall categories using a constant comparative method.

Results: The literature search yielded thirty-one papers: twenty-eight primary studies and three systematic reviews. Twelve themes were identified and synthesized into four overall problem categories, such as “Lack of professional recognition.” Interviews confirmed findings from the literature, but also revealed unidentified needs specific to CFS-sufferers in Québec. Policy recommendations were provided to address these needs.

Conclusions: Multi-Source Synthesis provides a systematic method for synthesizing data from original studies with literature findings, thereby broadening the knowledge base and the local relevance of decisions concerning specific patient populations.

Copyright © Cambridge University Press 2011

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