Measuring the bias against low-income country research: an Implicit Association Test
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.207 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it