It's time for Canadian community early warning systems for illicit drug overdoses
Bibliographic record
Abstract
Although fatal and non-fatal overdoses represent a significant source of morbidity and mortality, current systems of surveillance and communication in Canada provide inadequate measurement of drug trends and lack a timely response to drug-related hazards. In order for an effective early warning system for illicit drug overdoses to become a reality, a number of elements will be required: real-time epidemiologic surveillance systems for illicit drug trends and overdoses, inter-agency networks for gathering data and disseminating alerts, and mechanisms for effectively and respectfully engaging with members of drug using communities. An overdose warning system in an urban area like Vancouver would ideally be imbedded within a system that monitors drug trends and overdoses by incorporating qualitative and quantitative information obtained from multiple sources. Valuable information may be collected and disseminated through community organizations and services associated with public health, emergency health services, law enforcement, medical laboratories, emergency departments, community-based organizations, research institutions and people with addiction themselves. The present paper outlines considerations and conceptual elements required to guide implementation of such systems in Canadian cities such as Vancouver.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".