Who Tries to Find Objective Information on Health Care? Findings From the 2010 Health Confidence Survey
Bibliographic record
Abstract
This paper looks at who currently uses information on health cost, quality, and outcomes, specifically examining differences in information seeking by demographics, health status, health insurance coverage, and opinions about the U.S. health care system. Data comes from the EBRI/MGA 2010 Health Confidence Survey (HCS), a survey that examines a broad spectrum of health care issues, including Americans’ satisfaction with health care today, their confidence in the future of the health care system and the Medicare program, and their attitudes toward health care reform. The 2010 HCS asked a series of questions for the first time regarding whether an individual had ever tried to find objective information on various aspects of cost, quality, and outcomes. Overall, 45 percent of the population reported having tried to find information about the advantages and disadvantages of different treatments, while only 14 percent tried to find information about the number of disciplinary actions taken against a doctor or hospital. About one-quarter tried to find cost information (28 percent for the full costs of different treatments; 24 percent for the costs of different doctors and hospitals). Women, younger individuals, and individuals with higher levels of education were more likely than others to seek information on cost, quality, and access. Individuals who experience an increase in either premiums or cost sharing are more likely than those who do not to seek information. Plan sponsors can use this information to better engage workers and their families. The PDF for the above title, published in the February 2011 issue of EBRI Notes, also contains the fulltext of another February 2011 EBRI Notes article abstracted on SSRN: “Labor-Force Participation Rates of the Population Age 55 and Older: What Did the Recession Do to the Trends?”
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".