Utilization of Emergency Management and Research Institute (108 EMRI): An Emergency Response Service in Khammam District, Andhra Pradesh, India
Bibliographic record
Abstract
Emergency Management and Research Institute (EMRI) operates 1-0-8, 24X7 emergency service for medical, police and fire emergencies in India. Born in the context of the ineffective co-ordination among multiple emergency response systems, it was introduced on 15th of August 2005, with a fleet of 652 ambulances within the state of Andhra Pradesh (AP) as a Public Private Partnership. Objectives of this work were to study the utilization pattern of 108-EMRI services in Khammam district of AP, the demographic profile of the beneficiaries and the types of emergency services provided by 108-EMRI, using data provided by the District Coordinator of 108-EMRI. Secondary data was taken from the District Coordinator of 108-EMRI for a period of one year (June 2008 to May 2009). A total of 60,898 beneficiaries had availed the108-EMRI services in the four divisions of Khammam district. Among the beneficiaries, majority were pregnancy related (17.9%), followed by RTA (Road Traffic Accidents) (13.8%), suicides (6.7%) and cardiac emergencies (3.9%). EMRI is undoubtedly operationally effective system in reaching out to those who are in need. To its credit goes the achievement of bringing Emergency medical response on to the agenda of the nation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".