Bibliographic record
Abstract
Objectives: This study investigates the relationship between nurse staffing levels and differences in patient outcomes in terms of average length of stay, in-hospital mortality rate and 30-day death rate in order to evaluate the effectiveness of a policy that differentiates fees for inpatients on the basis of nurse-to-bed ratios. Methods We obtained information on inpatients from health insurance claims data published by the Health Insurance Review and Assessment Service(HIRA) in 2008, organizational factors(type of hospital, ownership) from the records of the hospital report system in 2008, and nurse staffing levels, which were graded on a scale of 1 to 7, from data compiled between December 15, 2007, and September 20, 2008. The data were segregated according to type of hospital and quarter and finally 3,517 records of 1,182 hospitals were analyzed using multi-level analysis. Results The average length of stay in grade 1∼6 hospitals was lower than that in grade 7 ones, but the difference was much below one day. No significant difference was found among different grades in tertiary hospitals. Further, variations in staffing levels did not result in any significant difference in the in-hospital mortality rate and 30-day death rate. Conclusions High nurse staffing levels did not result in better patient outcomes compared with low staffing levels. We therefore recommend modifying the above nurse staffing policy so as to make it more effective in improving patient outcomes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".