What Core Competency Training Skills Improve Mass Gathering Medical Staff Preparedness? An International Delphi Study for Expert Consensus
Bibliographic record
Abstract
OBJECTIVES: Mass Gathering Medicine focuses on mitigating issues at Mass Gathering Events. Medical skills can vary substantially among staff, and the literature provides no specific guidance on staff training. This study highlights expert opinions on minimum training for medical staff to formalize preparation for a mass gathering. METHODS: This is a 3-round Delphi study. Experts were enlisted at Mass Gathering conferences, and researchers emailed participation requests through Stat59 software. Consent was obtained verbally and on Stat59 software. All responses were anonymous. Experts generated opinions. The second and third rounds used a 7-point linear ranking scale. Statements reached a consensus if the responses had a standard deviation (SD) of less than or equal to 1.0. RESULTS: Round 1 generated 137 open-ended statements. Seventy-three statements proceeded to round 2. 28.7% (21/73) found consensus. In round 3, 40.3% of the remaining statements reached consensus (21/52). Priority themes included venue-specific information, staff orientation to operations and capabilities, and community coordination. Mass casualty preparation and triage were also highlighted as a critical focus. CONCLUSIONS: This expert consensus framework emphasizes core training areas, including venue-specific operations, mass casualty response, triage, and life-saving skills. The heterogeneity of Mass Gatherings makes instituting universal standards challenging. The conclusions highlight recurrent themes of priority among multiple experts.
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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 | 0.107 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".