Vital Signs: Health Insurance Coverage and Health Care Utilization —
Bibliographic record
Abstract
Background: The increasing number of persons in the United States with no health insurance has implications both for individual health and societal costs. Because of cost concerns, millions of uninsured persons forgo some needed health care, which can lead to poorer health and potentially to greater medical expenditures in the long term. Methods: CDC analyzed data from the National Health Interview Survey (NHIS) for 2006, 2007, 2008, and 2009 and early release NHIS data from the first quarter of 2010 to determine the number of persons without health insurance or with gaps in coverage and to assess whether lack of insurance coverage was associated with increased levels of forgone health care. Data were analyzed further by demographic characteristics, family income level, and selected chronic conditions. Results: In the first quarter of 2010, an estimated 59.1 million persons had no health insurance for at least part of the year before their interview, an increase from 58.7 million in 2009 and 56.4 million in 2008. Of the 58.7 million in 2009, 48.6 million (82.8%) were aged 18–64 years. Among persons aged 18–64 years with family incomes two to three times the federal poverty level (approximately $43,000–$65,000 for a family of four in 2009), 9.7 million (32.1%) were uninsured for at least part of the preceding year. Persons aged 18–64
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".