Evidence-Based Review on Medical Risk Management in Canada -To provide evidence for establishing a medical risk precaution and monitoring system in China
Bibliographic record
Abstract
Objective To provide evidence for establishing a medical risk precaution and monitoring system in China by evidence-based evaluation of the medical risk monitoring and precaution system in Canada, including the current situation and corresponding prevention measures. Method According to the unified search strategy made by our research group, we searched relevant databases and official or government websites. We included articles about medical risk management, medical error and patient safety in Canada. The included articles were classified and the quality was ranked. Results A total of 15 articles were included, among which 10 were official documents (about 2/3) and 5 described research methods (about 1/3), mainly involving medical risk management or evaluation, medical error and patient safety. In 2002, Canada established its National Steering Committee on Patient Safety (NSCPS) and proposed the suggestions to integrate health care resources accross the country, build a patient safety system, and established the Canada Patient Safety Institution (CPSI) to improve patient safety. Canada revised the patient safety management system, collected and issued related information, strengthened doctor-patient communication, developed continuous education for medical staff, practiced the best medical behavior and model and improved lawsuit procedures. These activities have achieved great progress in practice. Conclusions What have been done in Canada will provide a guide for us to establish scientific patient safety system and promote public awareness of patient safety.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".