Consumer Experiences in a Consumer‐Driven Health Plan
Bibliographic record
Abstract
OBJECTIVE: To assess the experience of enrollees in a consumer-driven health plan (CDHP). DATA SOURCES/STUDY SETTING: Survey of University of Minnesota employees regarding their 2002 health benefits. STUDY DESIGN: Comparison of regression-adjusted mean values for CDHP and other plan enrollees: customer service, plan paperwork, overall satisfaction, and plan switching. For CDHP enrollees only, use of plan features, willingness to recommend the plan to others, and reports of particularly negative or positive experiences. PRINCIPAL FINDINGS: There were significant differences in experiences of CDHP enrollees versus enrollees in other plans with customer service and paperwork, but similar levels of satisfaction (on a 10-point scale) with health plans. Eight percent of CDHP enrollees left their plan after one year, compared to 5 percent of enrollees leaving other plans. A minority of CDHP enrollees used online plan features, but enrollees generally were satisfied with the amount and quality of the information provided by the CDHP. Almost half reported a particularly positive experience, compared to a quarter reporting a particularly negative experience. Thirty percent said they would recommend the plan to others, while an additional 57 percent said they would recommend it depending on the situation. CONCLUSIONS: Much more work is needed to determine how consumer experience varies with the number and type of plan options available, the design of the CDHP, and the length of time in the CDHP. Research also is needed on the factors that affect consumer decisions to leave CDHPs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".