Bibliographic record
Abstract
This paper reviews safety initiatives in the health systems of the UK, Canada, Australia, and the US. Initiatives to tackle safety shortcomings involve public-private collaborations. Patient safety agencies (to institute learning, action and safety culture), adverse event reporting and, to a lesser extent, safety related performance indicators are currently used to design safer health systems. Their benefits are mixed, but there is little debate as to their possible side effects. Foreseeable adverse effects of multiple safety organisations stem from them being too many, too vague, too narrowly focused, threatened by the medical practice environment, and too optimistic. Safety related performance indicators are most developed in the US but suffer from inadequacies of administrative data, underreporting, variable indicator definitions, "extended" use, and low sensitivity of the diagnosis coding system, and arguable preventability of the prescribed conditions. A critical appraisal of the implications of these deficiencies is important to assure the safety of current health system safety initiatives and to establish evidence based safety. It is necessary to embed health system safety (as well as patient safety) in the societal culture, structures, and policies which promote effective, user centred, high performance care while allowing for healthy innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".