Bibliographic record
Abstract
This chapter examines the paradigm that a health care system’s behavior may be examined using an agent simulation in order to illuminate its macroscopic characteristics and the effects of policy on its over all operation. Further, if the individual components are well articulated, the component behavior may be also studied. Health care systems in North America are generally regulated by various processes and mechanisms in order to provide orderly access to, and control of, the health care system. While all processes are designed to be fair and equitable, in many ways the system can not be examined or optimized because the risk, that making changes to the system might result in degraded services, is too great to permit making even simple changes. In this context we propose the development of a health care system model in which agents mimic the behavior of the key components of the system. These components interact and engage each other in a manor analogous to the operation of the health care system. The formulation of such a system is, by its very nature, an extremely complex process, and necessitates development in components or units. In this chapter we present the first components of such a system. Each component has unique and complex behaviors. These components will, with additional development, form the basic structure of a health care system model. Specifically we present results from the development of a diabetic patient agent model, the development of an agent-based neurosurgery ward bed allocation system, and the development of an agent-based scheduling system that may be used to allocate resources within the health care system.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".