Intersectionality, health equity, and EDI: What’s the difference for health researchers?
Bibliographic record
Abstract
Many countries adopted comprehensive national initiatives to promote equity in higher education with the goal of transforming the culture of research. Major health research funders are supporting this work through calls for projects that focus on equity, resulting in a proliferation of theoretical frameworks including "intersectionality," "health equity," and variations of equity, diversity and inclusion, or EDI. This commentary is geared at individual principal investigators and health research teams who are developing research proposals and want to consider equity issues in their research, perhaps for the first time. We present histories and definitions of three commonly used frameworks: intersectionality, health equity, and EDI. In the context of health research, intersectionality is a methodology (a combination of epistemology and techniques) that can identify the relationships among individual identities and systems of oppression; however, it should also be used internally by research teams to reflect on the production of knowledge. Health equity is a societal goal that operationalizes the social determinants of health to document and address health disparities at the population level. EDI initiatives measure and track progress within organizations or teams and are best suited to inform the infrastructure and human resourcing "behind the scenes" of a project. We encourage researchers to consider these definitions and strive to tangibly move health research towards equity both in the topics we study and in the ways we do research.
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 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 | 0.040 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".