Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Hospitals face an increasing pressure toward efficiency and cost reduction while ensuring patient safety. This warrants a closer examination of the trade-off between production and protection posited in the literature for a high-risk hospital setting (intensive care). PURPOSES: On the basis of extant literature and concepts on both safety management and organizational/safety culture, this study investigates to which extent production pressure (i.e., increased staff workload and capacity utilization) and safety culture (consisting of safety climate among staff and safety tools implemented by management) influence the occurrence of medical errors and if/how safety climate and safety tools interact. METHODOLOGY/APPROACH: A prospective, observational, 48-hour cross-sectional study was conducted in 57 intensive care units. The dependent variable is the incidence of errors affecting those 378 patients treated throughout the entire observation period. Capacity utilization and workload were measured by indicators such as unit occupancy, nurse-to-patient/physician-to-patient ratios, levels of care, or NEMS scores. The safety tools considered include Critical Incidence Reporting Systems, audits, training, mission statements, SOPs/checklists, and the use of barcodes. Safety climate was assessed using a psychometrically validated four-dimensional questionnaire.Linear regression was employed to identify the effects of the predictor variables on error rate as well as interaction effects between safety tools and safety climate. FINDINGS: Higher workload has a detrimental effect on safety, whereas safety climate-unlike the examined safety tools-has a virtually equal opposite effect. Correlations between safety tools and safety climate as well as their interaction effects on error rate are mostly nonsignificant. PRACTICE IMPLICATIONS: Increased workload and capacity utilization increase the occurrence of medical error, an effect that can be offset by a positive safety climate but not by formally implemented safety procedures and policies.
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.
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 | 0.002 | 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 | 0.001 | 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) | 0.002 | 0.005 |
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 it