Analysis of the social consequences and value implications of the Everyday Discrimination Scale (EDS): implications for measurement of discrimination in health research
Bibliographic record
Abstract
The Everyday Discrimination Scale (EDS) is one of the most widely used measures of discrimination in health research, and has been useful for capturing the impact of discrimination on health. However, psychometric analysis of this measure has been predominantly among Black Americans, with limited examination of its effectiveness in capturing discrimination against other social groups. This paper explores the theoretical and historical foundations of the EDS, and draws on the analytic framework of Messick's theory of unified validity to examine the effectiveness of the EDS in capturing diverse experiences of discrimination. Encompassing both social consequences and value implications, Messick's unified validity contends that psychometric evaluation alone is insufficient to justify instrument use or ensure social resonance of findings. We argue that despite the robust psychometric properties and utility in addressing anti-Black race-related discrimination, the theoretical foundations and research use of the EDS have yet to respond to current discrimination theory, particularly intersectionality. This paper concludes with guidance for researchers in using the EDS in health research across diverse populations, including in data collection, analysis, and presentation of findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".