The Evaluation of Time Performance in the Emergency Response Center to Provide Pre-Hospital Emergency Services in Kermanshah
Bibliographic record
Abstract
This study evaluated the time performance in the emergency response center to provide pre-hospital emergency services in Kermanshah. This study was a descriptive retrospective cross-sectional study. In this study 500 cases of patients from Shahrivar (September) 2012 to the end of Shahrivar (September) 2013 were selected and studied by the non-probability quota method. The measuring tool included a preset cases record sheet and sampling method was completing the cases record sheet by referring to the patients' cases. Data were analyzed using SPSS version 18 and the concepts of descriptive and inferential statistics (Kruskal-Wallis test, benchmark Eta (Eta), Games-Howell post hoc test). The results showed that the interval mean between receiving the mission to reaching the scene, between reaching the scene to moving from the scene, and between moving from the scene to a health center was 7.28, 16.73 and 7.28 minutes. The overall mean of time performance from the scene to the health center was 11.34 minutes. Any intervention in order to speed up service delivery, reduce response times, ambulance equipment and facilities required for accuracy, validity and reliability of the data recorded in the emergency dispatch department, Continuing Education of ambulance staffs, the use of manpower with higher specialize levels such as nurses, supply the job satisfaction, and increase the coordination with other departments that are somehow involved in this process can provide the ground for reducing the loss and disability resulting from traffic accidents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".