Defining the terminology of first responders alerted for out-of-hospital cardiac arrest by medical dispatch centres: An international consensus study on nomenclature
Bibliographic record
Abstract
Aim: Emergency medical services target to reduce time to cardiopulmonary resuscitation and defibrillation by alerting additional individuals to out-of-hospital cardiac arrest (OHCA). Multiple terms are used to describe these individuals, potentially causing confusion and hindering comparisons. This international consensus study aimed to establish standardised terminology. Methods: Forty-six interdisciplinary researchers from four continents participated in a symposium on "Community First Responders" with the objective of standardising relevant terminology. Initially, terms were proposed anonymously for individuals alerted during work hours and those alerted during leisure time. Each term was rated on a 5-point Likert scale. Terms receiving a high level of agreement were included in the final voting process. Results: Seven terms were suggested for individuals alerted during work hours. In the first voting "first responder", "professional first responder", and "on-duty first responder" achieved high agreement. Ultimately, consensus was reached on the term "on-duty first responder".For individuals alerted during leisure time, ten terms were proposed. Among these, "first responder", "citizen first responder", "community emergency responder", "community first responder", "volunteer first responder", "volunteer responder", and "volunteer community first responder" reached high agreement. In the final vote "community first responder" was selected.The consensus group agreed that the overarching term "first responder" should be used to describe all community-based individuals, who are alerted, regardless of whether they are on duty or off duty. Conclusion: This consensus study recommends using the terms "on-duty first responder" and "community first responder" to describe individuals additionally alerted by medical dispatch centres to facilitate early intervention in OHCA.
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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 | 0.230 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".