Bibliographic record
Abstract
Purpose The aim of this review is to examine factors that may explain why other industries are considered ultrasafe while progress toward preventing adverse events in health care is not considered to have reached that level. Design/methodology/approach The paper is a narrative review. Findings Despite a decade of intense effort, the problem of patient harm in health care facilities remains a challenge. A recent study of ten hospitals in North Carolina, which have actively engaged in patient safety initiatives, reported rates of adverse events similar to those in the Institute of Medicine report, To Err Is Human in 1999. Seven key issues and their interaction are described. Research limitations/implications This review focuses on broad issues that likely impede progress generally, not on individual project or individual hospital program success stories. Originality/value The authors believe the difficulty in making significant headway on the patient safety agenda is due in part to the fact that it was always going to be a long (indeed never ending) struggle – aviation for example took almost 60 years to become ultra‐safe – and in part to misunderstanding the nature of the dynamics that are involved in the generation of adverse events in risk critical industries. The paper reflects on the nature of the safety initiatives that health care has tended to focus on, but which have not sufficiently taken note of central concepts of safety science, as well as on features of the health care system itself that have impeded, in the authors' view, progress on enhancing patient safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.012 | 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".