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 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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".