Bibliographic record
Abstract
preferences, health insurance Objective To promote managed competition in Dutch health insur-ance, the insured are now able to change heaith insurers. They can choose a health insurer with a low flat-rate premium, the best supplementary insurance and/or the best service. As we do not know why people prefer one health insurer to another, we investigated their reasons for selecting their health insurer and assessed the importance of the supplementary benefit package and the flat-rate premium. Methods A self-administered questionnaire was completed by 468 of a total of 884 (52.9%). Data were compared among three groups. The first group comprised those who left one health insurer for another (exit). The second group had joined the health insurer (entry) and the third group comprised those who did not switch (stayers). Results Those in the entry group were statistically significantly less satisfied with their former insurance organization than those in the other groups (exit and stayers) with the insurance organization under investigation. They were also less satisfied than the other groups in respect of the flat-rate premium. Those in the exit group were younger and seemed to be in better health. In general, the insured were only aware of small differences between health insurance funds and the three groups did not differ from each other in this respect. About a quarter of the entry group reported the flat-rate premium as a reason for selecting a particular health insurance fund. However, the most frequently reported reason, for both exit and entry, was the benefit package of the supplementary insurance. Conclusions In the absence of clear differences between insurance organizations, the advantages of managed competition maybe too difficult to achieve.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.837 | 0.591 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".