Exploring International Views on Key Concepts for Mass-gathering Health through a Delphi Process
Bibliographic record
Abstract
Introduction The science underpinning mass-gathering health (MGH) is developing rapidly. However, MGH terminology and concepts are not yet well defined or used consistently. These variations can complicate comparisons across settings. There is, therefore, a need to develop consensus and standardize concepts and data points to support the development of a robust MGH evidence-base for governments, event planners, responders, and researchers. This project explored the views and sought consensus of international MGH experts on previously published concepts around MGH to inform the development of a transnational minimum data set (MDS) with an accompanying data dictionary (DD). Report A two-round Delphi process was undertaken involving volunteers from the World Health Organization (WHO) Virtual Interdisciplinary Advisory Group (VIAG) on Mass Gatherings (MGs) and the MG section of the World Association for Disaster and Emergency Medicine (WADEM). The first online survey tested agreement on six key concepts: (1) using the term "MG HEALTH;" (2) purposes of the proposed MDS and DD; (3) event phases; (4) two MG population models; (5) a MGH conceptual diagram; and (6) a data matrix for organizing MGH data elements. Consensus was defined as ≥80% agreement. Round 2 presented five refined MGH principles based on Round 1 input that was analyzed using descriptive statistics and content analysis. Thirty-eight participants started Round 1 with 36 completing the survey and 24 (65% of 36) completing Round 2. Agreement was reached on: the term "MGH" (n=35/38; 92%); the stated purposes for the MDS (n=38/38; 100%); the two MG population models (n=31/36; 86% and n=30/36; 83%, respectively); and the event phases (n=34/36; 94%). Consensus was not achieved on the overall conceptual MGH diagram (n=25/37; 67%) and the proposed matrix to organize data elements (n=28/37; 77%). In Round 2, agreement was reached on all the proposed principles and revisions, except on the MGH diagram (n=18/24; 75%). Discussion/Conclusions Event health stakeholders require sound data upon which to build a robust MGH evidence-base. The move towards standardization of data points and/or reporting items of interest will strengthen the development of such an evidence-base from which governments, researchers, clinicians, and event planners could benefit. There is substantial agreement on some broad concepts underlying MGH amongst an international group of MG experts. Refinement is needed regarding an overall conceptual diagram and proposed matrix for organizing data elements. Steenkamp M , Hutton AE , Ranse JC , Lund A , Turris SA , Bowles R , Arbuthnott K , Arbon PA . Exploring international views on key concepts for mass-gathering health through a Delphi process. Prehosp Disaster Med. 2016;31(4):443-453.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".