Conceptualizing the Impact of Special Events on Community Health Service Levels: An Operational Analysis
Bibliographic record
Abstract
Mass gatherings (MG) impact their host and surrounding communities and with inadequate planning, may impair baseline emergency health services. Mass gatherings do not occur in a vacuum; they have both consumptive and disruptive effects that extend beyond the event itself. Mass gatherings occur in real geographic locations that include not only the event site, but also the surrounding neighborhoods and communities. In addition, the impact of small, medium, or large special events may be felt for days, or even months, prior to and following the actual events. Current MG reports tend to focus on the events themselves during published event dates and may underestimate the full impact of a given MG on its host community. In order to account for, and mitigate, the full effects of MGs on community health services, researchers would benefit from a common model of community impact. Using an operations lens, two concepts are presented, the "vortex" and the "ripple," as metaphors and a theoretical model for exploring the broader impact of MGs on host communities. Special events and MGs impact host communities by drawing upon resources (vortex) and by disrupting normal, baseline services (ripple). These effects are felt with diminishing impact as one moves geographically further from the event center, and can be felt before, during, and after the event dates. Well executed medical and safety plans for events with appropriate, comprehensive risk assessments and stakeholder engagement have the best chance of ameliorating the potential negative impact of MGs on communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".