A Simulation Optimization Approach to Long-Term Care Capacity Planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes a methodology for setting long-term care capacity levels over a multiyear planning horizon to achieve target wait time service levels. Our approach integrates demographic and survival analysis, discrete event simulation, and optimization. Based on this methodology, we developed a decision support system for use in practice. We illustrate this approach through two case studies: one for a regional health authority in British Columbia, Canada, and the other for a long-term care facility. We also compare our approach to the fixed ratio approach used in practice and the SIPP (stationary, independent, period by period) and MOL (modified offered load) approaches developed in the call center literature. Our results suggest that our approach is preferable. The fixed ratio approach lacks a rigorous foundation, and the SIPP and MOL approaches do not perform reliably mainly because of long service times. We conclude the paper with policy recommendations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it