Mass gathering healthcare and safety: current knowledge and future directions
Bibliographic record
Abstract
Introduction Mass gathering medicine (MGM) has been a rapidly evolving area of expertise for many years, with pearls and pitfalls emerging from recent literature. Gaps have been identified: lack of a pragmatic definition of MGM, weaknesses in data sets used to report on major events, difficulties with developing tools to help organizers assess health and safety issues. This study aims to map and describe the current body of literature on mass gatherings in order to identify strengths and weaknesses in the healthcare response, guiding future research in the field. Methods This study is a bibliometric review. Using the broad research terms “mass gatherings” and “major planned events”, Pubmed, Web of Science and Google Scholar were searched for publications up to Sept. 13, 2024. No restrictions of language or type of articles were used. All gray literature was included. After removal of duplicates, two independent reviewers confirmed relevance, and articles were organized according to date of publication, type of event (7), main subjects categories (11). Further keywords analyses have been conducted using VOSViewer (v.1.6.20). Results Initial database searches yielded 4,526 results. After the removal of duplicates and non-related articles, 898 publications remained and have been categorized, dating from 1978 to 2024. Full text analysis was possible for a total of 854 articles. More than 25% of the articles concerned religious events, while categories like infectious diseases and public health were the main subject of more than one third of the papers. Music events, spontaneous gatherings and environmental/remote events were underrepresented. Main topics like heat-related illnesses and environmental health, training and psychosocial aspects were also underrepresented. Keywords analysis allowed for the identification of areas of scarce knowledge that need further development. Conclusion Mass gathering health and safety literature has grown rapidly over the past years. This study identified areas of strong knowledge to build upon, while more scarce areas like psychosocial aspects, quality evaluation, management and supervision, training and development of formal expertise are still in need of improvement.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".