The Design and Evaluation of Emergency Call Taking User Interfaces for Next Generation 9-1-1
Bibliographic record
Abstract
In the coming years, emergency calling services in North America will begin to incorporate new modalities for reporting emergencies, including video-based calling and picture sharing. The challenge is that we know little of how future call-taking systems should be designed to support emergency calls with rich multimedia and what benefits or challenges they might bring. We have conducted three studies, along with design work, as part of our research to address this problem. First, we conducted observations and contextual interviews within three emergency response call centers to investigate call taking practices and reactions to the incorporation of rich multimedia in emergency call taking practices. Following this, we created user interface design mock-ups and conducted two additional studies with call takers. One involved low-fidelity designs and one involved the use of a medium-fidelity digital prototype. Across the studies, our results show that 9-1-1 call takers will need a next generation interface that supports multimedia, including video calling, as part of calls. Yet user interfaces will need to be different from commercial video conferencing applications that are commonplace today. Design features for 9-1-1 systems must focus on supporting camera work and the capture of emergency scenes; situational awareness of incidents across call takers, including current and historical media associated with them; and, the regulation of media flow to balance privacy concerns and the viewing of potentially traumatic visuals.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".