Evaluation of the Effect of New Health Reform Policy of China Based on Prospect Theory
Bibliographic record
Abstract
Abstract Background: New health reform policy of China has gone through more than ten years from 2009 to 2019, it has come a critical period. It is important to evaluate the effect of health system reform policy, but there are a few existing studies and the researchers have not considered the impact of psychological factors on person decision-making. This study using the prospect theory can overcome this defect. Let the evaluation of the effect of new medical reform policy of China more scientific and reasonable. Methods: Data on the effect of the new health reform from China Health Statistics Yearbook and National Bureau of Statistics of China were obtained. And the indicators were selected by Corrected Item Total Correlation and Cronbach’s α reliability coefficient. The selected indicator data were calculated by the prospect theory model. Meanwhile the gray relation analysis method is introduced to enlarge the difference between the advantages and disadvantages to making the comprehensive evaluation result more obvious. Results: The results of comprehensive evaluation of the effects of health reform policies in China fluctuated from 2000 to 2017. From 2000 to 2004, the fluctuation range of the comprehensive prospect value is large; from 2004 to 2009, the comprehensive prospect value shows a slow downward trend; from 2009 to 2012, the comprehensive prospect value has decreased slightly, and then it has rebounded substantially; from 2012 to 2014, the comprehensive prospect value is stable; and from 2014 to 2017, the comprehensive prospect value is declining. Conclusions: This study showed that the implementation of new health reform policy of China has a significant impact on China's medical and health system. The new health reform advanced hard in exploration. It is worth affirming that the overall goal and direction of the new health reform are correct, but the practice is unsatisfactory. The releasing and implementation of supporting policy have the important influence on the progress of the new health reform. And the effect of the policy will decrease with the increase of the total amount. Therefore, scientific and effective late management can ensure that the policy continues to play its role.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".