Nurse Staff Scheduling Optimization Model in the Emergency Room of the Hospital Escuela Universitario
Bibliographic record
Abstract
The present study has focused on developing a proposal to optimize nursing staff allocation in the emergency room of the Hospital Escuela Universitario in Tegucigalpa, Honduras, using integer linear programming.The primary objective has been to maximize the number of shifts assigned to all staff.In this context, a thorough analysis of the current allocation process has been conducted, identifying specific areas requiring improvements to ensure an equitable distribution of shifts and address the particular needs of the emergency area.Various tools and methodologies have been employed for data collection and the design of the mathematical model.A case study has been conducted using a mixed approach, with a predominance of quantitative analysis.During this process, variables, and constraints necessary for effectively modelling the problem have been precisely defined.Integer linear programming has been the main tool for developing a mathematical model to optimize shift allocation, utilizing OpenSolver in Microsoft Excel.The validity of the model has been verified through consultations with experts in the field and pilot testing of the instruments used.A significant finding of this study is that the current staff quantity is sufficient to carry out equitable shift assignments, suggesting that no additional hiring is required at the moment.However, it is important to note that the proposal considers the number of nurses, types of shifts, and related constraints as known data, allowing for deterministic modelling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".