Persisting problems related to race and ethnicity in public health and epidemiology research
Bibliographic record
Abstract
A recent and comprehensive review of the use of race and ethnicity in research that address health disparities in epidemiology and public health is provided. First it is described the theoretical basis upon which race and ethnicity differ drawing from previous work in anthropology, social science and public health. Second, it is presented a review of 280 articles published in high impacts factor journals in regards to public health and epidemiology from 2009-2011. An analytical grid enabled the examination of conceptual, theoretical and methodological questions related to the use of both concepts. The majority of articles reviewed were grounded in a theoretical framework and provided interpretations from various models. However, key problems identified include a) a failure from researchers to differentiate between the concepts of race and ethnicity; b) an inappropriate use of racial categories to ascribe ethnicity; c) a lack of transparency in the methods used to assess both concepts; and d) failure to address limits associated with the construction of racial or ethnic taxonomies and their use. In conclusion, future studies examining health disparities should clearly establish the distinction between race and ethnicity, develop theoretically driven research and address specific questions about the relationships between race, ethnicity and health. One argue that one way to think about ethnicity, race and health is to dichotomize research into two sets of questions about the relationship between human diversity and health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.016 | 0.031 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".