The odd couple: using biomedical and intersectional approaches to address health inequities
Bibliographic record
Abstract
BACKGROUND: Better understanding and addressing health inequities is a growing global priority. OBJECTIVE: In this paper, we contribute to the literature examining complex relationships between biological and social dimensions in the field of health inequalities. Specifically, we explore the potential of intersectionality to advance current approaches to socio-biological entwinements. DESIGN: We provide a brief overview of current approaches to combining both biological and social factors in a single study, and then investigate the contributions of an intersectional framework to such work. RESULTS: We offer a number of concrete examples of how intersectionality has been used empirically to bring both biological and social factors together in the areas of HIV, post-traumatic stress disorder, female genital circumcision/mutilation/cutting, and cardiovascular disease. CONCLUSION: We argue that an intersectional approach can further research that integrates biological and social aspects of human lives and human health and ultimately generate better and more precise evidence for effective policies and practices aimed at tackling health inequities.
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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 | 0.036 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".