Equity of What in Healthcare? Why the Traditional Answers Don't Help Policy - and What to Do in the Future
Bibliographic record
Abstract
Introduction There are many deep philosophical issues regarding equity that I will slide over in order to address some practicalities of equity policy (see, for deeper material, Olsen 1997; Wikler and Murray forthcoming). However, I do want to try to link theory and policy rather than keep them in their usual silos. This is a dangerous plan. My amateur ethics will strike serious philosophers as gravely deficient, while my amateur policy strategizing will strike decision-makers as distantly up in the clouds. However, in the spirit of “nothing ventured ...” I am going to try to link the two more directly than is usual. One reason for doing this is that, if we cannot discuss ethics explicitly as a foundation of policies for equity in health and healthcare policy, then I doubt we can do it anywhere else. A second reason is that I think there is a chance, if we can be more explicit about our ethics, that we might manage to translate them into policy action in reasonable and doable ways. Another reason is that I am fairly confident that the reasonable and doable ways will be different from the current ways. A fourth is that leaving the ethics largely implicit means that the huge differences between us that might otherwise remain submerged could become underwater reefs with the potential to rip the bottoms out of well-meaning policies for equity in practice – as soon as it becomes clear that one person’s Equity of What in Healthcare? Why the Traditional Answers Don’t Help Policy – and What to Do in the Future
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".