Safer Care - Measuring to Manage and Improve
Bibliographic record
Abstract
Developing Information for Improving Safetyearly as the 17th century BC, Hammurabi's Code acknowledged that harm might result from medical care.Interest in measuring patient safety to support quality improvement emerged more recently, but is by no means new.Around 1910, for example, Ernest Codman advocated a focus on "end results," taking comprehensive measurements during and following care in order to help prevent undesirable outcomes.Similarly, Florence Nightingale documented survival rates for surgical patients during the Crimean War.Fast-forward to today and patient safety is on the agenda worldwide.In Canada the first nationwide study of adverse events in hospitals was published in 2004 (Baker et al. 2004).Healthcare providers, the new Canadian Patient Safety Institute, provincial institutes and task forces, and many others are working to respond to the results of the study.While medical practice has changed since the days of Ernest Codman, what has not changed is the focus on having good information to guide quality improvement efforts.Measures are required at a variety of levels (see Figure 1).For instance, broad-based global metrics provide information about the prevalence of adverse events and their impact on patients.Healthcare organizations often seek to track patient safety outcomes for their patients, as well as related processes of care.Individual quality improvement teams also require detailed information to monitor their progress in specific areas.This information may be collected as part of rapid cycle improvement or other change processes and will evolve over time depending on the focus of quality improvement efforts.
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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 | 0.043 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".