On the Way Out: An Analysis of Patient Transfers from Four, Large-Scale, North American Music Festivals Over Two Years
Bibliographic record
Abstract
Introduction: Music festivals are globally attended events that bring together performers and fans for a defined period of time. These festivals often have onsite medical care to help reduce the impact on local healthcare systems. Historically, the literature suggests that patient transfers offsite are frequently related to complications of substance use. However, there is a gap in understanding as to why patients are transferred to a hospital when an onsite medical team, providing a higher level of care (HLC), is present. Aim: To better understand the causes that necessitate patient transportation to the hospital during festivals that have onsite physician-led coverage. Methods: De-identified patient data from a convenience sample of four, large-scale Canadian festivals (over two years) were extracted. Patient encounters that resulted in transfers to hospital, by ambulance, non-emergency transport vehicle (NETV), or self-transportation were analyzed for this study. Results: Each festival had an onsite medical team that included physicians, nurses, and paramedics. During 34 event days, there were 10,406 patient encounters, resulting in 156 patients requiring transfer to a hospital. A patient presentation rate of 16.5/1,000 was observed. The ambulance transfer rate was 0.12/1,000 of attendees. The most common reason for transport was musculoskeletal injuries (54%) that required imaging. Discussion: The presence of onsite teams capable of treating and releasing patients impacted the case mix of patients transferred to a hospital, and may reduce the number of transfers for intoxication. Confounding preconceptions, patients in the present study were transferred largely for injuries that required imaging. Results suggest that a better understanding of the specific effects onsite medical teams have on avoiding off-site transfers will aid in improving planning for music festivals. Findings also identify areas for further improvement in care, such as onsite radiology, which could potentially further reduce the impact of music festivals on local health services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".