Bibliographic record
Abstract
This article defines the problem of the uninsured. It begins with an overview of why health insurance matters and presents a profile of the uninsured. It then discusses the roles and limits of private and public health insurance as sources of coverage for the nonelderly population. The article concludes with reflections on the current health insurance environment and prospects for reform. The large and growing number of uninsured people is of concern because health coverage makes a difference in whether and when people get necessary medical care, where they get their care, and ultimately how healthy people are. About a quarter of uninsured adults (26 percent) say that they have postponed seeking care in the past year because of its cost, compared to about 6 percent of privately insured adults (Figure 1). In addition, more than half of uninsured adults (54 percent) have no usual source of care, compared to 1 in 10 adults (10 percent) with other types of coverage. Similarly, uninsured children are more likely to lack a usual source of care, delay care, or to have unmet medical needs than children with insurance. The uninsured are less likely to receive timely preventive and outpatient care and are more likely to be hospitalized for avoidable health problems. Improved access to care through health insurance ultimately has an impact on people’s health and lives: an estimated 22,000 excess deaths occurred among adults aged 25-64 in 2006 as a result of lack of health coverage.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".