Can health equity survive epidemiology? Standards of proof and social determinants of health
Bibliographic record
Abstract
OBJECTIVE: This article examines how epidemiological evidence is and should be used in the context of increasing concern for health equity and for social determinants of health. METHOD: A research literature on use of scientific evidence of "environmental risks" is outlined, and key issues compared with those that arise with respect to social determinants of health. RESULTS: The issue sets are very similar. Both involve the choice of a standard of proof, and the corollary need to make value judgments about how to address uncertainty in the context of "the inevitability of being wrong," at least some of the time, and to consider evidence from multiple kinds of research design. The nature of such value judgments and the need for methodological pluralism are incompletely understood. CONCLUSION: Responsible policy analysis and interpretation of scientific evidence require explicit consideration of the ethical issues involved in choosing a standard of proof. Because of the stakes involved, such choices often become contested political terrain. Comparative research on how those choices are made will be valuable.
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 | 0.403 | 0.633 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.113 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".