Bibliographic record
Abstract
is widely accepted that the desired improvements in patient safety require a change in the culture within healthcare (CPSI 2004; IOM 2000; NPSA 2004). The Institute of Medicine (IOM) report “To Err Is Human” concluded that “the status quo is no longer acceptable ... Health care organizations must develop a culture of safety” (IOM 2000: 14). In the UK, building a safety culture is the first step of the National Patient Safety Agency’s (NPSA) seven-step guide to improving patient safety. In Canada, safety culture is one of the Canadian Council on Health Services Accreditation’s (CCHSA) five patient safety goals and required organizational practices. It is therefore important that senior administrators and clinical managers have a sound understanding of safety culture, so that they can make informed decisions about improvement strategies. The recognition of the importance of cultural factors is based on research conducted in other high reliability industries such as nuclear power and petrochemical processing. The investigation into the Chernobyl disaster concluded that a poor safety culture at the facility was a significant causal factor. The Advisory Committee on the Safety of Nuclear Installations produced the most widely accepted definition of safety culture.
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 | 0.146 | 0.151 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.013 | 0.021 |
| Research integrity | 0.019 | 0.051 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".