How does the patient to nurse ratio relate to the quality of patient care and nurse burnout in the hospital?
Bibliographic record
Abstract
The patient-to-nurse ratio is a topic that affects all nurses. A review of the research literature was performed to study this vital issue. Data was obtained from surveys conducted in numerous countries, including the United States, Canada, the United Kingdom, Taiwan, and Chile. The evidence showed that an increased patient-to-nurse ratio motivated nurses’ intention to leave their job. A higher patient-to-nurse ratio was found to have been associated with higher levels of personal burnout, client-related burnout, and job dissatisfaction in nurses. Currently in California, in medical-surgical units, the registered nurse staffing mandate is at one nurse to five patients. When nurses’ workloads were in line with California’s mandated ratios, nurses’ burnout and job dissatisfaction were lower, and nurses reported consistently better quality of care. Furthermore, there was a decrease in nurses receiving verbal abuse from patients or other staff and complaints from patients and their family. In addition, a theoretical model is presented, which offers a hypothesis that nurses’ level of education may be a factor in affecting patient outcomes. In this paper, I propose a study to examine how a baccalaureate degree in nursing may affect patient mortality and failure to rescue.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".