Emergency preparedness & incident command systems in residential life at University of California at Los Angeles
Bibliographic record
Abstract
Currently the Office of Residential Life (ORL) at University of California at Los Angeles??? supports the safety and well being of over 10,000 students living in on-campus housing. It is vital to have efficient safety and security protocols in the event of an emergency or natural disaster. Even though there are policies, procedures and training for Professional staff within the department, the first responders are required to collaborate with several other agencies such as the University police department, community service officers, residential hall access monitors, counseling and psychological services, emergency medical services and many other external agencies to react quickly to student emergent or day to day needs. \nThe major issue is that at this time there is not a standard incident management system that allows all organizations involved to respond or communicate in a uniform manner. Student Staff and/or Professional staffs generally are called to respond in emergency incidents, although UCLA is interested in implementing the use of Incident Command Systems (ICS) to help improve incident response, especially as it relates to natural disaster preparedness. The Resident Directors, whom lead emergency response in ORL are required to be trained on Disaster Preparedness, to improve emergency response. Table top exercises were implemented winter quarter to educate the staff on the basics of ICS followed by an evaluation of the Resident Directors knowledge of ICS, emergency response as well as readiness to take action in the event of emergency. It was found that most Resident Directors were ready to respond, but had little to no understanding of ICS. It was concluded that professional staff and student staff needed more training prior to the opening of fall 2011, so the safety and security committee implemented training sessions throughout August and September to help in the process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".