International Consensus on Key Concepts and Data Definitions for Mass-gathering Health: Process and Progress
Bibliographic record
Abstract
Mass gatherings (MGs) occur worldwide on any given day, yet mass-gathering health (MGH) is a relatively new field of scientific inquiry. As the science underpinning the study of MGH continues to develop, there will be increasing opportunities to improve health and safety of those attending events. The emerging body of MG literature demonstrates considerable variation in the collection and reporting of data. This complicates comparison across settings and limits the value and utility of these reported data. Standardization of data points and/or reporting in relation to events would aid in creating a robust evidence base from which governments, researchers, clinicians, and event planners could benefit. Moving towards international consensus on any topic is a complex undertaking. This report describes a collaborative initiative to develop consensus on key concepts and data definitions for a MGH "Minimum Data Set." This report makes transparent the process undertaken, demonstrates a pragmatic way of managing international collaboration, and proposes a number of steps for progressing international consensus. The process included correspondence through a journal, face-to-face meetings at a conference, then a four-day working meeting; virtual meetings over a two-year period supported by online project management tools; consultation with an international group of MGH researchers via an online Delphi process; and a workshop delivered at the 19thWorld Congress on Disaster and Emergency Medicine held in Cape Town, South Africa in April 2015. This resulted in an agreement by workshop participants that there is a need for international consensus on key concepts and data definitions.
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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 | 0.702 | 0.633 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.038 | 0.042 |
| Open science | 0.022 | 0.049 |
| Research integrity | 0.020 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".