The Second Opinion Dispatch- Evaluating Decisions Made by an Ambulance Dispatch Center
Bibliographic record
Abstract
BACKGROUND: The efficacy and precision of dispatching right ambulance to right patient demands a proper decision-making. Wrong decisions may lead to overloading of emergency departments and shortage of ambulances. The aim of this study was to evaluate and improve the process of prehospital resource allocation conducted by a Swedish ambulance dispatch center.METHODS: A team of three experienced ambulance and dispatch nurses evaluated the decisions made by the dispatch center. The method chosen was “Action Research” divided into five actions during 76 days. In the first action, the team listened passively to the calls. The team gradually increased its involvement in the process of decision-making during the actions.RESULTS: During the actions, specific keywords indicating a need for evaluation were identified. The results showed a need to change the primary decisions in 486 cases out of 24,800 calls (2%). The most common measure after an evaluation was to change an ordinary ambulance transportation to an assessment vehicle staffed by a nurse or a physician who would select an appropriate care level (hospital vs. primary healthcare).CONCLUSION: This model not only optimized the prehospital resources but also changed the process of decision-making at the dispatch center and improved their staffs’ ability to optimize the allocation of emergency resources.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".