Hospital Nurse Staffing and Patient Mortality, Emotional Exhaustion, and Job Dissatisfaction
Bibliographic record
Abstract
OBJECTIVE: To conduct an investigation similar to a landmark study that investigated the association between nurse-to-patient ratio and patient mortality, failure-to-rescue, emotional exhaustion and job satisfaction of nurses. METHODS: Cross-sectional analysis of 2709 general, orthopedic, and vascular surgery patients, and 140 staff nurses (42% response rate) caring for these patients in a large Midwestern institution. The main outcome measures were mortality, failure-to-rescue, emotional exhaustion, and job dissatisfaction. RESULTS AND CONCLUSIONS: Staffing was not a significant predictor of mortality or failure-to-rescue, nor did clinical specialty predict emotional exhaustion or job dissatisfaction. Although these findings reinforce adequate staffing ratios at this institution, programs that support nurses in their daily practice and positively impact job satisfaction need to be explored. The Nursing Research Council not only has heightened awareness of how staffing ratios affect patient and nurse outcomes, but also a broader understanding of how the research process can be used to effectively shape nurse's practice and work environments.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".