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      Measuring the bias against low-income country research: an Implicit Association Test

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          Abstract

          Background

          With an increasing array of innovations and research emerging from low-income countries there is a growing recognition that even high-income countries could learn from these contexts. It is well known that the source of a product influences perception of that product, but little research has examined whether this applies also in evidence-based medicine and decision-making. In order to examine likely barriers to learning from low-income countries, this study uses established methods in cognitive psychology to explore whether healthcare professionals and researchers implicitly associate good research with rich countries more so than with poor countries.

          Methods

          Computer-based Implicit Association Test (IAT) distributed to healthcare professionals and researchers. Stimuli representing Rich Countries were chosen from OECD members in the top ten (>$36,000 per capita) World Bank rankings and Poor Countries were chosen from the bottom thirty (<$1000 per capita) countries by GDP per capita, in both cases giving attention to regional representation. Stimuli representing Research were descriptors of the motivation (objective/biased), value (useful/worthless), clarity (precise/vague), process (transparent/dishonest), and trustworthiness (credible/unreliable) of research. IAT results are presented as a Cohen’s d statistic. Quantile regression was used to assess the contribution of covariates (e.g. age, sex, country of origin) to different values of IAT responses that correspond to different levels of implicit bias. Poisson regression was used to model dichotomized responses to the explicit bias item.

          Results

          Three hundred twenty one tests were completed in a four-week period between March and April 2015. The mean Implicit Association Test result (a standardized mean relative latency between congruent and non-congruent categories) for the sample was 0.57 (95% CI 0.52 to 0.61) indicating that on average our sample exhibited moderately strong implicit associations between Rich Countries and Good Research. People over 40 years of age were less likely to exhibit pro-poor implicit associations, and being a peer reviewer contributes to a more pro-poor association.

          Conclusions

          The majority of our participants associate Good Research with Rich Countries, compared to Poor Countries. Implicit associations such as these might disfavor research from poor countries in research evaluation, evidence-based medicine and diffusion of innovations.

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          Most cited references42

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          Measuring individual differences in implicit cognition: the implicit association test.

          An implicit association test (IAT) measures differential association of 2 target concepts with an attribute. The 2 concepts appear in a 2-choice task (2-choice task (e.g., flower vs. insect names), and the attribute in a 2nd task (e.g., pleasant vs. unpleasant words for an evaluation attribute). When instructions oblige highly associated categories (e.g., flower + pleasant) to share a response key, performance is faster than when less associated categories (e.g., insect & pleasant) share a key. This performance difference implicitly measures differential association of the 2 concepts with the attribute. In 3 experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black + pleasant vs. White + pleasant for self-described unprejudiced White subjects).
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            Measuring individual differences in implicit cognition: The implicit association test.

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              Randomized response: a survey technique for eliminating evasive answer bias.

              S L Warner (1965)
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                Author and article information

                Contributors
                m.harris@imperial.ac.uk
                jmacinko@ucla.edu
                geronimo.jimenez@gmail.com
                pm1393@nyu.edu
                Journal
                Global Health
                Global Health
                Globalization and Health
                BioMed Central (London )
                1744-8603
                6 November 2017
                6 November 2017
                2017
                : 13
                : 80
                Affiliations
                [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Institute of Global Health Innovation, , Imperial College London, ; 10th Floor, QEQM building, St. Mary’s Campus, Praed Street, London, W2 1NY England
                [2 ]ISNI 0000 0000 9632 6718, GRID grid.19006.3e, UCLA Fielding School of Public Health, , Center for Health Sciences, ; 650 Charles E. Young Dr. South, Room 31-235B, Los Angeles, CA 90095-1772 USA
                [3 ]ISNI 0000 0001 2224 0361, GRID grid.59025.3b, Centre for Population Health Sciences (CePHaS), Lee Kong Chian School of Medicine, , Nanyang Technological University, ; 11 Mandalay Road Level 18 Clinical Sciences Building, Novena Campus, 308232 Singapore, Singapore
                [4 ]ISNI 0000 0004 1936 8753, GRID grid.137628.9, NYU College of Global Public Health, ; 726 Broadway, New York, NY 10012 USA
                Author information
                http://orcid.org/0000-0002-0005-9710
                Article
                304
                10.1186/s12992-017-0304-y
                5674740
                29110668
                19f5c2ed-0ff4-4198-8d5b-117ff8fb0151
                © The Author(s). 2017

                Open AccessThis 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.

                History
                : 2 February 2017
                : 19 October 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000905, Commonwealth Fund;
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Health & Social care
                implicit association test,bias,research evaluation,stereotypes,reverse innovation

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