Canadian Patient Safety Champions: Collaborating on Improving Patient Safety
Bibliographic record
Abstract
Patients for Patient Safety Canada champions have grown in numbers and purpose since their initiation into the World Health and Pan-American Health Organizations' Patients for Patient Safety initiative in May 2006. The 25 Canadian patients and family members not only share their adverse event experiences but are actively engaged in collaboration with health professionals, administrators and decision-makers to initiate proactive patient safety strategies. Their intention is to have their stories heard as tools for learning. They also wish to raise local, regional and national awareness of patient safety problems. The different patient and family stories and experiences share some common issues and suggested solutions that might make a difference in patient safety. One key solution is involving patients and families not only in discussions of treatment and follow-up when adverse events occur but also proactively on patient safety advisory committees. These actions would acknowledge a common interest in seeing that the right things are done. Patients and families share the common interest of all those advocating for patient safety, namely, First do no harm (attributed to Hippocrates, circa 470-360 B.C.). The patients and families of Patients for Patient Safety Canada are a group of committed, dedicated individuals who should be acknowledged for sharing their experiences and trying to make a difference in 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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".