Recommendations for action: a community meeting in preparation for a mass-casualty opioid overdose event in Southeastern Ontario
Bibliographic record
Abstract
Given the steady rise of overdose morbidity and mortality in North America, and increasing frequency of sudden clusters of non-fatal and fatal overdoses in other jurisdictions, regional preparedness plans to respond effectively to clusters of overdoses may reduce the impact of such events on the population. On the 27th of February 2017 in Kingston, Ontario, KFL&A Public Health, in collaboration with public health partners, hosted a full-day workshop involving table-top exercises and discussions for service partners on how to prepare for, respond to, and manage a mass-casualty event secondary to opioid overdose in Southeastern Ontario. The workshop assisted in identifying the various challenges faced by service partners, provided an understanding of the roles and responsibilities of partner agencies, and helped to determine next steps in preparation to address a mass opioid overdose situation at the local level. This report suggests key roles and responsibilities of partners involved in responding to a mass-casualty event secondary to opioid overdose, recommendations to address the feedback and challenges raised throughout the workshop, and a protocol to help determine when to activate an Incident Management System (IMS).
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".