Toolkit article: Approaches to measuring social inequities in health in human biology research
Bibliographic record
Abstract
Across populations, human morbidity and mortality risks generally follow clear gradients, with socially-disadvantaged individuals and groups tending to have higher morbidity and mortality at all life stages relative to those more socially advantaged. Anthropologists specialize in understanding the proximate and ultimate factors that shape variation in human biological functioning and health and are therefore well-situated to explore the relationships between social position and health in diverse ecological and cultural contexts. While human biologists have developed sophisticated methods for assessing health using minimally-invasive methods, at a disciplinary level, we have room for conceptual and methodological improvement in how we frame, measure, and analyze the social inequities that might shape health inequities. This toolkit paper elaborates on some steps human biologists should take to enhance the quality of our research on health inequities. Specifically, we address: (1) how to frame unequal health outcomes (i.e., inequalities vs. disparities vs. inequities) and the importance of identifying our conceptual models of how these inequities emerge; (2) how to measure various axes of social inequities across diverse cultural contexts, and (3) approaches to community collaboration and dissemination. We end by discussing (4) future directions in human biology research of health inequities, including understanding the ultimate causes of sensitivity to social inequities and transitioning from research to action.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".