Bibliographic record
Abstract
Introduction: As the second largest metropolitan area in Canada, Montréal has its share of risks for disasters and major incidents. In such events, the interoperability of emergency services is critical to effective interventions. As the emergency medical service (EMS) for the cities of Montréal and Laval, the Urgences-santé Corporation (USC) has close ties with several emergency partners on the territory, including police and fire departments. These different organizations have joined forces to develop a tabletop exercise program (TEP) to train operational managers to initiate a better-coordinated response on joint interventions. Aim: The TEP was designed to enhance interoperability in the field by improving communication and the understanding of the roles, responsibilities, methods of coordination and decision-making in each of the organizations involved. The aim is for all of USC’s operational managers to participate in at least one exercise of the TEP within the first year of the program. Methods: Selection criteria were established to gather, for each exercise, managers that are likely to work with one another on a real intervention. The TEP was also designed in such a way that its implementation would require few resources and yield minimal impact on regular operations. Results: After four pilot exercises to fine-tune the approach, the program was launched on October 5, 2018. We have now run eight exercises, each involving one or more USC supervisor. The response has been very favorable from the participants as well as their directors. Discussion: In the short term, the TEP helps managers understand their counterparts’ key issues, and has already yielded improvements in our joint interventions. In the longer term, the program will help identify specific training needs to better equip responders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.105 | 0.022 |
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".