Le droit à la sécurité des soins dans le contexte du projet de loi 113
Bibliographic record
Abstract
In 1999, the Institute of Medicine published «To Err is Human: Building a Safer Health System ». According to the authors, medical errors account for 44,000 to 98, 000 deaths annually in the United States. During the same period, in Quebec, a series of deadly medical errors occurred. Consequently, a ministerial committee was put in place to evaluate the problem of medical errors. According to this committee, the ratio of medical errors in Quebec was of the same order as those observed in the United States and other countries. Bill 113, which is the "An Act to amend the Act respecting health services and social services as regards the safe provision of health services and social services ", was put in place following the committee 's recommendations. Bill 113 increases the obligation for health care institutions to pay more attention to patient safety and risk management and provide safe treatment throughout the entire health care process. In adopting Bill 113, the legislator recognises the patient 's right to receive safe care, guarantees openness in the health care system and also ensures the implementation of a risk management structure. However, the implementation of bill 113 was not easy and there is still need for improvement. Consequently, we must continue with our efforts to develop a culture of safety in health care. Bill 113 is the first step towards improving 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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".