Identifying health interventions by in-event health services at mass gathering events to improve health service outcomes: A scoping review
Bibliographic record
Abstract
Introduction Mass gathering events can lead to an increased incidence of injury and/or illness for attendees, compared to expected rates in the host community. Health interventions provided by in-event health services at mass gathering events can reduce the demand on local health system. This paper aims to identify the in-event health interventions used by health providers at mass gathering events. Methods This scoping review was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation. Analysis focused on mass gathering events that had an in-event health service providing at least one health intervention. Interventions were deductively coded against the surge capacity domains of staff, stuff/supplies, space, and systems. Results A total of 41 papers were included in this review. The median patient presentation rate was 6.2/1,000 attendees. The median transport to hospital rate was 0.35/1,000. The most frequently reported interventions against the four ‘S’ surge capacity domains included: medications (n=23/41, 56.1%), procedural interventions (n=23/41, 56.1%) in stuff/supplies; presence of medical doctors (n=34/41, 82.9%) in staff; health support (n=18/41, 43.9%) in systems; and mobile (n=23/41, 56.1%) or temporary structures (n=21/41, 51.2%) in spaces. Conclusion This review provides an overview of possible health interventions that in-event health service providers can utilise. The health interventions provided by in-event health service at mass gathering events should be considered as one part of a broader health approach. Future research evaluation should include a cost of health interventions and their benefits to health systems.
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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 | 0.024 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".