The Event Chain of Survival in the Context of Music Festivals: A Framework for Improving Outcomes at Major Planned Events
Bibliographic record
Abstract
Despite the best efforts of event producers and on-site medical teams, there are sometimes serious illnesses, life-threatening injuries, and fatalities related to music festival attendance. Producers, clinicians, and researchers are actively seeking ways to reduce the mortality and morbidity associated with these events. After analyzing the available literature on music festival health and safety, several major themes emerged. Principally, stakeholder groups planning in isolation from one another (ie, in silos) create fragmentation, gaps, and overlap in plans for major planned events (MPEs). The authors hypothesized that one approach to minimizing this fragmentation may be to create a framework to "connect the dots," or join together the many silos of professionals responsible for safety, security, health, and emergency planning at MPEs. Adapted from the well-established literature regarding the management of cardiac arrests, both in and out of hospital, the "chain of survival" concept is applied to the disparate groups providing services that support event safety in the context of music festivals. The authors propose this framework for describing, understanding, coordinating and planning around the integration of safety, security, health, and emergency service for events. The adapted Event Chain of Survival contains six interdependent links, including: (1) event producers; (2) police and security; (3) festival health; (4) on-site medical services; (5) ambulance services; and (6) off-site medical services. The authors argue that adapting and applying this framework in the context of MPEs in general, and music festivals specifically, has the potential to break down the current disconnected approach to event safety, security, health, and emergency planning. It offers a means of shifting the focus from a purely reactive stance to a more proactive, collaborative, and integrated approach. Improving health outcomes for music festival attendees, reducing gaps in planning, promoting consistency, and improving efficiency by reducing duplication of services will ultimately require coordination and collaboration from the beginning of event production to post-event reporting. Lund A , Turris SA . The Event Chain of Survival in the context of music festivals: a framework for improving outcomes at major planned events. Prehosp Disaster Med. 2017;32(4):437-443.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| 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".