Becoming a high reliability organization
Bibliographic record
Abstract
Aircraft carriers, electrical power grids, and wildland firefighting, though seemingly different, are exemplars of high reliability organizations (HROs)--organizations that have the potential for catastrophic failure yet engage in nearly error-free performance. HROs commit to safety at the highest level and adopt a special approach to its pursuit. High reliability organizing has been studied and discussed for some time in other industries and is receiving increasing attention in health care, particularly in high-risk settings like the intensive care unit (ICU). The essence of high reliability organizing is a set of principles that enable organizations to focus attention on emergent problems and to deploy the right set of resources to address those problems. HROs behave in ways that sometimes seem counterintuitive--they do not try to hide failures but rather celebrate them as windows into the health of the system, they seek out problems, they avoid focusing on just one aspect of work and are able to see how all the parts of work fit together, they expect unexpected events and develop the capability to manage them, and they defer decision making to local frontline experts who are empowered to solve problems. Given the complexity of patient care in the ICU, the potential for medical error, and the particular sensitivity of critically ill patients to harm, high reliability organizing principles hold promise for improving ICU patient care.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".