Research on the development model of commercial health insurance based on big data
Bibliographic record
Abstract
Despite the rapid development of commercial health insurance in China, compared with developed countries, there are still gaps in product types, product design, risk control, preferential tax policies, business models and consumer value-added services. In recent years, everything around us has been "digitized", and emerging concepts and technologies such as smart medical care, Internet of Things health care, and mobile medical care have attracted the general attention of the medical and health industry and the information and communication industry, and are being widely used. With the rapid development of big data, it brings opportunities for the development of commercial health insurance. It not only changes the market-oriented product design, but also pays more attention to customer needs. It also provides data support for the accurate pricing of commercial health insurance, and forms health intervention for consumers in the whole process before and after the event, which makes it possible to promote the sound and rapid development of commercial health insurance. Based on this background, this paper intends to study the development model of commercial health insurance products under the background of big data.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".