Dispatch Decision Making in an Air Medical Transport System
Bibliographic record
Abstract
Air medical transport relies on effective dispatching of air and land vehicles to provide the fastest and best care possible for patient transfers. These difficult dispatch decisions are characterized by high time pressure, uncertainty, and the dynamic and complex environment of medical transportation. This paper describes a preliminary study of the decision making processes that occur during dispatch decisions at Ornge, the air medical transportation system in Ontario, Canada. We drew upon the Critical Decision Method and the structured data analysis approach to understand the major decision points faced by Ornge’s dispatchers, and the cues and sources of information attended to in those situations. We found that the decision points deal with three main goals: maintain situation awareness, match resource to transfer, and plan logistics of transfer. Furthermore, we found that time estimation might play an important role in helping dispatchers coordinate within the dispatch team and with their external partners. These findings may help improve the design of computer aided dispatch software to better support the goals of the dispatchers.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".