Nurse staffing and cost of care in adult intensive care units in a university hospital in Thailand
Bibliographic record
Abstract
Decisions about nurse staffing levels in intensive care units (ICUs) should be guided by research to ensure optimal outcomes. This descriptive correlational study in a large Thai hospital was designed to evaluate the effect of nurse staffing levels on the costs of care, in terms of medical care cost per patient day and health personnel cost per patient day, in ICUs. The costing data were collected prospectively from the records of 242 critically ill patients while the nurse staffing levels were extracted from hospital management reports. The findings showed that a nurse staffing model with a higher number of registered nurses (RNs) led to an increase in the health personnel cost per patient day. However, a greater number of RNs was associated with improved patient safety and efficiency, thereby reducing the length of stay and the costs of care in the long term. This study provides evidence to support decisions by hospital administrators concerning RN staffing levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".