Can automated content analysis be used to assess and improve the use of evidence in mental health policy? A systematic review

Kristel Alla; Florin I Oprescu; Wayne D Hall; Harvey A Whiteford; Brian W Head; Carla S Meurk

doi:10.1186/s13643-018-0853-z

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Can automated content analysis be used to assess and improve the use of evidence in mental health policy? A systematic review

Journal article

Open access

Peer reviewed

Can automated content analysis be used to assess and improve the use of evidence in mental health policy? A systematic review

Kristel Alla, Florin I Oprescu, Wayne D Hall, Harvey A Whiteford, Brian W Head and Carla S Meurk

Systematic Reviews, Vol.7, 194

2018

DOI: https://doi.org/10.1186/s13643-018-0853-z

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Abstract

automated content analysis

evidence-informed policy

research impact

mental health

wordscores

Background: This review assesses the utility of applying an automated content analysis method to the field of mental health policy development. We considered the possibility of using the Wordscores algorithm to assess research and policy texts in ways that facilitate the uptake of research into mental health policy. Methods: The PRISMA framework and the McMaster appraisal tools were used to systematically review and report on the strengths and limitations of the Wordscores algorithm. Nine electronic databases were searched for peerreviewed journal articles published between 2003 and 2016. Inclusion criteria were (1) articles had to be published in public health, political science, social science or health services disciplines; (2) articles had to be research articles or opinion pieces that used Wordscores; and (3) articles had to discuss both strengths and limitations of using Wordscores for content analysis. Results: The literature search returned 118 results. Twelve articles met the inclusion criteria. These articles explored a range of policy questions and appraised different aspects of the Wordscores method. Discussion: Following synthesis of the material, we identified the following as potential strengths of Wordscores: (1) the Wordscores algorithm can be used at all stages of policy development; (2) it is valid and reliable; (3) it can be used to determine the alignment of health policy drafts with research evidence; (4) it enables existing policies to e revised in the light of research; and (5) it can determine whether changes in policy over time were supported by the evidence. Potential limitations identified were (1) decreased accuracy with short documents, (2) words constitute the unit of analysis and (3) expertise is needed to choose 'reference texts'. Conclusions: Automated content analysis may be useful in assessing and improving the use of evidence in mental health policies. Wordscores is an automated content analysis option for comparing policy and research texts that could be used by both researchers and policymakers

Details

Title: Can automated content analysis be used to assess and improve the use of evidence in mental health policy? A systematic review
Authors: Kristel Alla (Author) - University of Queensland
Florin I Oprescu (Author) - University of the Sunshine Coast
Wayne D Hall (Author) - University of Queensland
Harvey A Whiteford (Author) - University of Queensland
Brian W Head (Author) - University of Queensland
Carla S Meurk (Author) - University of Queensland
Publication details: Systematic Reviews, Vol.7, 194; 16
Publisher: BioMed Central Ltd.
Date published: 2018
DOI: 10.1186/s13643-018-0853-z
ISSN: 2046-4053
Copyright note: Copyright © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Organisation Unit: University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy; School of Creative Industries - Legacy; Engage Research Lab; School of Health and Behavioural Sciences - Legacy; School of Health - Public Health
Language: English
Record Identifier: 99450876002621
Output Type: Journal article

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