Text Messaging as a Strategy to Address the Limits of Audio-Based Communication During Mass-Gathering Events with High Ambient Noise
Bibliographic record
Abstract
INTRODUCTION: The provision of medical care in environments with high levels of ambient noise (HLAN), such as concerts or sporting events, presents unique communication challenges. Audio transmissions can be incomprehensible to the receivers. Text-based communications may be a valuable primary and/or secondary means of communication in this type of setting. OBJECTIVES: To evaluate the usability of text-based communications in parallel with standard two-way radio communications during mass-gathering (MG) events in the context of HLAN. METHODS: This Canadian study used outcome survey methods to evaluate the performance of communication devices during MG events. Ten standard commercially available handheld smart phones loaded with basic voice and data plans were assigned to health care providers (HCPs) for use as an adjunct to the medical team's typical radio-based communication. Common text messaging and chat platforms were trialed. Both efficacy and provider satisfaction were evaluated. RESULTS: During a 23-month period, the smart phones were deployed at 17 events with HLAN for a total of 40 event days or approximately 460 hours of active use. Survey responses from health care providers (177) and dispatchers (26) were analyzed. The response rate was unknown due to the method of recruitment. Of the 155 HCP responses to the question measuring difficulty of communication in environments with HLAN, 68.4% agreed that they "occasionally" or "frequently" found it difficult to clearly understand voice communications via two-way radio. Similarly, of the 23 dispatcher responses to the same item, 65.2% of the responses indicated that "occasionally" or "frequently" HLAN negatively affected the ability to communicate clearly with team members. Of the 168 HCP responses to the item assessing whether text-based communication improved the ability to understand and respond to calls when compared to radio alone, 86.3% "agreed" or "strongly agreed" that this was the case. The dispatcher responses (n = 21) to the same item also "agreed" or "strongly agreed" that this was the case 95.5% of the time. CONCLUSION The use of smart phone technology for text-based communications is a practical and feasible tool for MG events and should be explored further. Multiple, reliable, discrete forms of communication technology are pivotal to executing effective on-site medical and disaster responses.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".