Bibliographic record
Abstract
Hospital checklists are gaining momentum, particularly since the World Health Organization's Safe Surgery Saves Lives Program published results of its study in 2009, indicating that a safety checklist significantly improved surgical outcomes in hospitals across the world. The South Carolina Hospital Association, in partnership with Dr Atul Gawande, has launched a program to implement the World Health Organization Surgical Safety Checklist in every operating room in the state over the next few years. Governments, in such places as France and the Canadian province of Ontario, are also stepping in to make surgical checklists mandatory in their hospitals. Drawing on research, recent initiatives, and the company's experience in high-acuity units, this article explores the implications and challenges of implementing checklists in today's hospitals. If a checklist is to succeed as a mechanism for transforming evidence-based care and safety protocols into best and actual practice, it needs to be used consistently and durably; to achieve this, hospitals need to foster a supportive environment as well as acquire a system to monitor, measure, and manage a culture that effectively embraces checklists.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".