Harper et al. Respond to "Measuring Social Disparities in Health"
Bibliographic record
Abstract
We appreciate Messer's thoughtful comments (1) on our article (2) and, broadly speaking, we agree that health disparities research and policymaking would benefit from increased attention to the issues of scale, interpretability, and causal relations in the measurement of health disparities. Like Messer (1), we have suggested using measures of absolute disparity, at least as a starting point for discussions of the size of health disparities, because they quantify the absolute burden of disease among disadvantaged populations and the potential gains to overall population health from reducing absolute disparities (3, 4). However, by way of clarification, her argument that absolute measures are preferable because they indicate the fraction of the disadvantaged population adversely affected is not necessarily true. Houweling et al. (5) show that absolute disparities and disease levels tend to have an inverse U-shaped association (differences tend to be larger when overall rates of disease are average and smaller at the extremes), whereas ratio measures generally decline with increasing overall prevalence.
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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 | 0.032 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.071 | 0.049 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".