Aller au bout de ses erreurs : vers une gestion du risque des accidents thérapeutiques
Bibliographic record
Abstract
In 1999, the Institute of Medicine succeeded to bring attention to a quality of care issue afflicting North American health care systems since at least half a century. According to the IOM, more than 100 000 patients die each year in the United States in consequence of a preventable adverse event. How does this issue affect the health system in Québec? In 2001, a report from the Québec ministry of health speculated that the problem of medical errors extends pass the borders of the United States to the Québec health care system. Faced with this reality, the Québec Legislative Assembly adopted, in December 2002, Bill 113 which aims for a more transparent and safe health care system. We must realize that most adverse events are the result of system failures, coupled with human error. These latent failures will never be detected and rectified without the intense collaboration of health care workers. Only the reporting of health care accidents and incidents will permit us to analyse those errors and than adopt mechanisms intercepting errors or, at the very least, minimising their consequences. However will Bill 113 achieve the legislator 's intention of diminishing the incidence of medical accidents in Québec? Will Québec health care workers declare all accidents and incidents discovered in the course of their medical interventions ? Ultimately, could the implantation of a no fault system help substitute our blame culture for a culture of transparence and quality and thus contribute to reduce the incidence of adverse events in our health care system? We all know that to err is human. However, most errors could be prevented by adopting new systems of care. Such changes are paramount to the quality of our medical care.
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.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".