Evaluating healthcare systems with insufficient capacity to meet demand
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
Modeling healthcare systems using discrete-event simulation (DES) provides the flexibility to analyze both their steady-state and transient performance. However, there has been little work on how best to measure healthcare system performance in cases where there is at least one unstable and lengthening queue in the system, so that traditional steady-state measures such as mean queue length or mean time in queue are meaningless. Using the example of an academic sleep disorders clinic, the authors discuss some of the challenges in constructing a DES model of a healthcare system that has a growing waiting list due to insufficient capacity in one or more areas. Specific considerations include: bottleneck identification through pre-analysis, how to determine a meaningful warm-up period, and the selection of performance measures given system instability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it