Research on the development model of commercial health insurance based on big data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it