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. While many Americans struggle to pay taxes, few understand how difficult it can be for needy Americans to enroll in public social benefits such as Temporary Assistance for Needy Families (one of many income support programs), Pell grants (one of many tuition assistance programs), or Medicaid (one of many public health insurance programs). Perhaps because such programs are difficult to enroll in, only a fraction of needy families receive the social benefits to which they are entitled. That percentage is smaller for those most in need (e.g., those with disabilities or caregiving responsibilities). In this editorial, 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 card 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".