Hippocrates Denied: Why Canada Has Yet to Act on the Patient Safety Imperative
Bibliographic record
Abstract
A review of patient safety literature reveals clear documentation of significant patient safety concerns in many countries going back over two decades. However, it was not until the publication of the Institute of Medicine's To Err Is Human about four years ago that widespread attention was at last drawn to this issue. Even with this attention, there has been a very limited response in Canada to the well-documented need for action to address preventable errors. After some reflection, it is clear that a whole series of factors may be conspiring to slow or blunt our response to this issue. This commentary explores these factors and endorses strategies for moving forward.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.084 | 0.070 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".