Fairness for Whom? Learning Health Systems’ Approach to Equity in Healthcare
Bibliographic record
Abstract
Many healthcare systems use "equity" as a catch-all term to underscore their commitment to delivering care matching users' needs. Despite its ubiquity, it is often haphazardly used and applied to care and improvement efforts. As the learning health systems (LHSs) approach gains prominence, LHS researchers have sought to embed equity into their work while navigating systems with differing views of equity. We examine several components of equity, its definitions within LHSs and knowledge from LHSs' equity approach that could be implemented across systems. We conclude by suggesting various ways in which readers can embed equity into their respective LHSs.
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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 | 0.074 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.082 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".