Core Curriculum for Event Medical Leaders
Bibliographic record
Abstract
Introduction: The literature on mass gatherings has expanded over the last decade. However, no readily accessible curriculum exists to prepare and support event medical leaders. Such a curriculum has the potential to align event medical professionals on improving event safety, standardizing emergency response, and reducing community impacts. Methods: We organized collaborative expert focus groups on the proposed “core curriculum” and “electives.” Results: Key features of a mass gathering medical curriculum include operations-focused, evidence-informed, best-known practices offered via low barrier, modular, flexible formats with interactive options, and a multi-national focus. Core content proposed: Background (Definitions, Context, Risk, Legalities) Event Medical Planning - “The Seven Steps” - (1.) Assessment and Environmental Scan - Event Emergency Action Plan, (2.) Human Resources, (3.) Equipment/Supplies, (4.) Infrastructure/Logistics, (5.) Transportation (To, On, From), (6.) Communication (Pre, During, Post), and (7.) Administration/Medical Direction Event After-Action Reporting Case-based Activities Electives mirror Core outline and serve as expanded case-studies of specific event categories. Initially proposed electives include: Concerts/Music Festivals Running Events Cycling Events Multi-Sport Events Obstacle Adventure Courses Staged Wilderness Courses Amateur Games Political Gatherings & Orations Religious Gatherings & Pilgrimages Community Gatherings (e.g., Parades, Fireworks, etc.) Discussion: Complex team learning to standardize real-world approaches has been accomplished in other medical domains (e.g., ACLS, AHLS, ATLS, PALS, etc.). A course for event medicine should not re-teach medical content (i.e. first aid, paramedicine, nursing, medicine); it should make available a commonly understood, systematic approach to planning, execution, and post-event evaluation vis a vis health services at events. A ‘train the trainer’ model will be required, with business operations support for sustainable course delivery. The author team seeks community feedback at WCDEM 2019 in creating ‘the ACLS’ of Event Medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".