A Critical Analysis of the Impact of the Medical Insurance System on the Relationship between Doctor and Patients- based on a comparative study of Canada and China
Bibliographic record
Abstract
Objective To explore the key dimensions of the impact of the medical insurance system on the relationship between doctors and patients in China. By comparing the design of different medical insurance systems and their influence on the relationship between doctors and patients in China and Canada, there are aims to improve doctor-patient relationships and to suggest policies. Methods A quantitative and qualitative analysis was conducted to compare the relevant medical insurance arrangements between China and Canada and to provide reasonable policy recommendations. Results The different institutional arrangements of financing mechanisms, payment mechanisms, reimbursement mechanisms, fund operation and management mechanisms, legislative supervision mechanisms and medical insurance elements in different places are the key system causes of the different results of the doctor-patient relationship in China and Canada. Conclusion The construction of a harmonious relationship between doctors and patients is not only the responsibility of doctors and patients, as the medical insurance system plays an important role in it. International experience shows that a reasonable institutional design and elements of the arrangements influences the doctor-patient relationship, to provide a good institutional environment. * Indicates faculty mentor.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".