Workers' Health Insurance: Trends, Issues, and Options to Expand Coverage
Bibliographic record
Abstract
In recent years, employer-sponsored health insurance has been eroding. An increasing number of working adults are without health insurance coverage, and forecasts indicate continuing declines in coverage. To reverse these trends and expand coverage for workers and their families, a range of public and private policy options are under discussion. The approaches vary in the extent to which they would build on the employment-based system, adapt the nongroup or individual market, or expand public programs. Many health coverage expansion policies would combine public and private approaches. Proposals in the 109th Congress address four major options: expansion of tax credits; creation of new federal–state roles in regulating insurance markets; expansion of purchasing options for small firms; and expansion of public programs for the under-65 population. This report was prepared for the Commonwealth Fund/Alliance for Health Reform 2006 Bipartisan Congressional Health Policy Conference. Support for this research was provided by The Commonwealth Fund. The views presented here are those of the author and not necessarily those of The Commonwealth Fund or its directors, officers, or staff, or of The Commonwealth Fund Commission on a High Performance Health System or its members. This and other Fund publications are online at www.cmwf.org. To learn more about new publications when they become available, visit the Fund’s Web site and register to receive e-mail alerts. Commonwealth Fund pub. no. 908.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".