ArticleMedical science educator2025
Uncovering Disparities in Medical Education: Advisory Guide of Strategies and Best Practices for Illuminating Blind Spots.
Article in Medical science educator, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment and grading are prone to disparities based on race and ethnicity. Health professions educators need a guide for how to strategically navigate assessment data. This monograph provides a consensus-driven advisory guide, drawing upon common challenges schools face when detecting, measuring, and analyzing gender/racial disparities, and shares practical algorithms and strategies for engaging with the data. Five steps are proposed: create a process diagram; identify assessment data sources and potential manifestations of disparities and inequities in assessments; consider other variables when analyzing data; choose analytical approaches to evaluate sources of disparities; and identify potential threats and challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.