Building a Digital Republic to Reduce Health Disparities and Improve Population Health in the United States
Bibliographic record
Abstract
Income, schooling, and healthcare are key ingredients for health, but most government programs that are designed to provide these social benefits are difficult to access, target those least in need, and carry enormous administrative costs. Benefits such as Temporary Assistance for Needy Families or Medicaid are difficult to enroll in, so only those who have the cognitive capacity to navigate the application process receive those benefits. The bureaucracies of welfare programs also increase the cost of administering the program. Redundancies in welfare programs also mean that there are redundant bureaucracies. In this commentary, we discuss a novel method for improving health while also improving privacy, reducing fraud, and improving data system compatibility. Specifically, we propose a digital identity credential that allows for the creation of a “digital republic” in which enrollment in social benefits can be automated, and the benefits can be targeted to those most in need. While there are large potential population health and health disparities benefits that could arise from a digital republic, more empirical work is needed to understand the extent to which nations have benefited from digital identity programs in the past and the political economy associated with implementing such programs.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".