Terminology and Definitions of Racial Health Equity in Prominent Health Websites: Systematic Review
Bibliographic record
Abstract
Background: The websites of prominent public health and health care organizations play pivotal roles in ensuring access to quality health information, including information guiding health equity. Several initiatives have been developed in the United States to promote equitable, fair, and inclusive health information and practices across prominent health websites. Currently, health disparities across racial groups are recognized as a critical public health problem. Simultaneously, the use of the term "racial health equity/equities" has been rising in academic literature. However, the definition and findability of "racial health equity/equities" information have not yet been evaluated in health websites. Thus, we used a systematic review approach to assess the findability and availability of racial health equity terminology and definitions across prominent health organization websites. Objective: The objective of this study was to systematically evaluate the definitions and findability of "racial health equity/equities and related terms" on prominent health organizations' websites. Methods: We conducted a systematic review of websites from government agencies, professional organizations, and selected health care organizations with relevance to the US health care system. Google and the US Digital Analytics program were used for initial searches. Definitions, terms, and accompanying citations for racial health equity terms, including "racial health inequity" or "racial health disparities," were extracted from all websites. A findability tool was developed to evaluate the ease of finding the terms and definitions, with ratings ranging from "very easy" to "very difficult." Additionally, we analyzed the themes and sentiments of the retrieved definitions. Results: We analyzed 69 websites from prominent health organizations. Approximately half (n=31) of the websites lacked any definitions for racial health equity and related terms, and of the 38 that included definitions, most did not include citations. The definitions varied across websites, and most were rated as "very difficult" to find. Conclusions: This study highlights the absence of a systematic, standardized, and accurate approach to organizing, defining, and presenting racial health equity information on prominent health websites. Specifically, there is a lack of consistent definitions for racial health equity and related terms across prominent health organization websites.
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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 | 0.027 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".