Optimization of Nurse Numbers in Emergency Department of a District Hospital in a Developing Country Iran, 2014
Bibliographic record
Abstract
<p class="zhengwen">Background: this study aims to optimize nurse numbers in emergency department of a district hospital in Iran using linear programming.</p><p class="zhengwen">Material and methods: through observation and checklist data about average patient arrival, delivery time of care services needed for patients and number of nurses were obtained. Using linear programming optimum number of nurse needed for right delivery of services was calculated.</p><p class="zhengwen">Results: optimum number of nurses was calculated as 12 nurses, but because of some issues in 3<sup>rd</sup> shift, real number of nurses needed for a 24 hours period is equal to 16 (12+4) nurses (12 as minimum needed plus nurses who stay and don’t leave the 3<sup>rd</sup> shift till tomorrow morning).</p>Conclusion: Using LP models can be useful for estimating optimum number of nurses for different wards of hospitals, so they can reduce their costs and reach more productivity.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".