Nurse Staffing, the Clinical Work Environment, and Burn Patient Mortality
Bibliographic record
Abstract
The complexity of modern burn care requires an integrated team of specialty providers working together to achieve the best possible outcome for each burn survivor. Nurses are central to many aspects of a burn survivor's care, including physiologic monitoring, fluid resuscitation, pain management, infection prevention, complex wound care, and rehabilitation. Research suggests that in general, hospital nursing resources, defined as nurse staffing and the quality of the work environment, relate to patient mortality. Still, the relationship between those resources and burn mortality has not been previously examined. This study used a multivariable risk-adjusted regression model and a linked, cross-sectional claims database of more than 14,000 adults (≥18 years) thermal burn patients admitted to 653 hospitals to evaluate these relationships. Hospital nursing resources were independently reported by more than 29,000 bedside nurses working in the study hospitals. In the high burn patient-volume hospitals (≥100/y) that care for the most severe burn injuries, each additional patient added to a nurse's workload is associated with 30% higher odds of mortality (P < .05, 95% CI: 1.02-1.94), and improving the work environment is associated with 28% lower odds of death (P < .05, 95% CI: 0.07-0.99). Nursing resources are vital in the care of burn patients and are a critical, yet previously omitted, variable in the evaluation of burn outcomes. Attention to nurse staffing and improvement to the nurse work environment is warranted to promote optimal recovery for burn survivors. Given the influence of nursing on mortality, future research evaluating burn patient outcomes should account for nursing resources.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".