Developing Public Health Initiatives through Understanding Motivations of the Audience at Mass-Gathering Events
Bibliographic record
Abstract
This report identifies what is known about audience motivations at three different mass-gathering events: outdoor music festivals, religious events, and sporting events. In light of these motivations, the paper discusses how these can be harnessed by the event organizer and Emergency Medical Services. Lastly, motivations tell what kinds of interventions can be used to achieve an understanding of audience characteristics and the opportunity to develop tailor-made programs to maximize safety and make long-lasting public health interventions to a particular "cohort" or event population. A lot of these will depend on what the risks/hazards are with the particular populations in order to "target" them with public health interventions. Audience motivations tell the event organizer and Emergency Medical Services about the types of behaviors they should expect from the audience and how this may affect their health while at the event. Through these understandings, health promotion and event safety messages can be developed for a particular type of mass-gathering event based on the likely composition of the audience in attendance. Health promotion and providing public information should be at the core of any mass-gathering event to minimize public health risk and to provide opportunities for the promotion of healthy behaviors in the local population. Audience motivations are a key element to identify and agree on what public health information is needed for the event audience. A more developed understanding of audience behavior provides critical information for event planners, event risk managers, and Emergency Medical Services personnel to better predict and plan to minimize risk and reduce patient presentations at events. Mass-gathering event organizers and designers intend their events to be positive experiences and to have meaning for those who attend. Therefore, continual vigilance to improve public health effectiveness and efficiency can become best practice at events. Through understanding the motivations of the audience, event planners and designers, event risk managers, and emergency medical personnel may be better able to understand the motivation of the audience and how this might impact on audience behavior at the event. Hutton A , Ranse J , Munn MB . Developing public health initiatives through understanding motivations of the audience at mass-gathering events. Prehosp Disaster Med. 2018;33(2):191-196.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".