The odd couple: using biomedical and intersectional approaches to address health inequities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it