Bibliographic record
Abstract
Health care operations management has become a major topic for health care service providers and society.Operations research already has and further will make considerable contributions for the effective and efficient delivery of health care services.This special issue collects seven carefully selected papers dealing with optimization and decision analysis problems in the field of health care operations management.The papers cover a considerable range of health care problems including location planning for hospital and health services ( Mestreet al., Zhang et al.), organization of hospital resources (Vanberkel et al., Hulshof et al.), surgery scheduling (Marques et al., Herring and Herrmann) and treatment scheduling (Schimmelpfeng et al.).These problems are addressed within a number of different health care environments such as hospitals, preventive care, outpatient clinics and rehabilitation hospitals.The operations research techniques which are employed are mixed-integer linear programming, stochastic dynamic programming, hierarchical decomposition, queueing theory, simulation and choice models.The special issue thus covers a broad range of problems, environments and techniques.It is noteworthy that all papers are either treating a real life problem or are inspired by the latter, which demonstrates the problem-driven
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".