Improving Data Quality in Mass-Gatherings Health Research
Bibliographic record
Abstract
Mass gatherings attract large crowds and can strain the planning and health resources of the community, city, or nation hosting an event. Mass-Gatherings Health (MGH) is an evolving niche of prehospital care rooted in emergency medicine, emergency management, public health, and disaster medicine. To explore front-line issues related to data quality in the context of mass gatherings, the authors draw on five years of management experience with an online, mass-gathering event and patient registry, as well as clinical and operational experience amassed over several decades. Here the authors propose underlying human, environmental, and logistical factors that may contribute to poor data quality at mass gatherings, and make specific recommendations for improvement through pre-event planning, on-site actions, and post-event follow-up. The advancement of MGH research will rely on addressing factors that influence data quality and developing strategies to mitigate or enhance those factors. This is an exciting time for MGH research as higher order questions are beginning to be addressed; however, quality research must start from the ground up to ensure optimal primary data capture and quality. Guy A , Prager R , Turris S , Lund A . Improving data quality in mass-gatherings health research. Prehosp Disaster Med. 2017;32(3):329-332.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".