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Global Health. 2017 Nov 6;13(1):80. doi: 10.1186/s12992-017-0304-y.

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

Author information

Institute of Global Health Innovation, Imperial College London, 10th Floor, QEQM building, St. Mary's Campus, Praed Street, London, W2 1NY, England.
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.
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.
NYU College of Global Public Health, 726 Broadway, New York, NY, 10012, USA.



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.


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.


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.


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.


Bias; Implicit association test; Research evaluation; Reverse innovation; Stereotypes

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