Bibliographic record
Abstract
Purpose: This study was conducted to explore the impact of nurse staffing level and oral care on pneumonia in elderly inpatients in long-term care hospitals (LTCHs). Methods: Data were obtained from the Health Insurance Review and Assessment Services (HIRA) including the profiles of LTCHs, monthly patient assessment reports and medical report survey data of pneumonia patients by HIRA in the fourth quarter of 2010. The sample consisted of 37 LTCHs and 6,593 patients. Results: Patient per nurse staff (OR=1.43, CI=1.22~1.68) and no oral care (OR=1.29, CI=1.01~1.64) were significantly related with hospital acquired pneumonia. The difference in percent of oral care by hospital was not significant between high and low group in nurse staffing level. Conclusion: In order to reduce the occurrence of pneumonia in eldery patients, effective nursing interventions are not only required but also nurse staffing levels that enable nurses to provide the intervention.
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".