Consumer Beliefs and Health Plan Performance: It's Not Whether You Are in an HMO but Whether You Think You Are
Bibliographic record
Abstract
Surveys that rate how persons enrolled in HMOs and other types of health coverage feel about their health care are used to bolster claims that HMOs provide inferior quality care, providing justification for patient protection legislation. This research illustrates that the conventional wisdom regarding inferior care in HMOs may color how people assess their health care in surveys, resulting in survey findings biased toward showing HMOs provide inferior care and reinforcing existing stereotypes. Using merged data from the Community Tracking Study Household and Insurance Followback surveys, we identify privately insured persons who correctly and incorrectly know what kind of health plan they are covered by. Nearly a quarter misidentified their type of health coverage. Differences between responses by HMO and non-HMO enrollees to questions covering satisfaction with health care and physician choice, the quality of the last physician's visit, and patient trust in their physician shrink or disappear when we control for beliefs about what type of plan they are covered by. Results suggest that researchers and policy makers should be cautious about using consumer surveys to assess the relative quality of care provided under different types of health insurance.
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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".