2014 Health and Voluntary Workplace Benefits Survey: Most Workers Continue to be Satisfied With Their Own Health Plan, but Growing Number Give Low Ratings to Health Care System
Bibliographic record
Abstract
Four years after passage of the Patient Protection and Affordable Care Act of 2010 (PPACA), implementation of many of its provisions and delay of others, an increasing majority of workers continue to give low marks to the U.S. health care system. This paper examines public opinion with respect to various aspects of the United States health care system using data from the 2014 EBRI/Greenwald & Associates Health and Voluntary Workplace Benefits Survey (WBS) as well as from the 1998-2012 EBRI/Greenwald & Associates Health Confidence Survey (HCS) and the 2013 WBS. The WBS and HCS examine a broad spectrum of health care issues, including workers’ satisfaction with health care today, their confidence in the future of the health care system and the Medicare program, and their attitudes toward benefits in the workplace. Asked to rate the U.S. health care system, many workers describe it as poor (29 percent) or fair (32 percent). One-quarter consider it good, while only a small minority rate it as very good (10 percent) or excellent (1 percent). Dissatisfaction with the health care system appears to be focused primarily on cost. The 2013-2014 EBRI/Greenwald & Associates Workplace Benefits Survey (WBS) and the 1998-2012 EBRI/Greenwald & Associates Health Confidence Survey (HCS) find that the percentage of workers rating the health care system as poor more than doubled between 1998-2006 (rising from 14 percent to 32 percent); however, while between 2006-2013 that percentage fell slightly, it jumped to 29 percent in 2014. In contrast to the ratings for the health care system overall, workers’ ratings of their own health plans continue to be generally favorable. One-half of those with health insurance coverage are extremely or very satisfied. While 47 percent of workers indicate they are extremely or very confident about their ability to get the treatments they need today, only 30 percent are confident about their ability to get needed treatments during the next 10 years, and just 19 percent are confident about this once they are eligible for Medicare. The PDF for the above title, published in the September 2014 issue of EBRI Notes, also contains the fulltext of another September 2014 EBRI Notes article abstracted on SSRN: “How Does Household Expenditure Change With Age for Older Americans?”
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".