At-a-glance - What can paramedic data tell us about the opioid crisis in Canada?
Bibliographic record
Abstract
The nature of Canada's opioid crisis necessitates additional data sources that can provide a more comprehensive picture of the epidemic, in order to provide public health officials and decision-makers with a robust evidence base. Paramedic data provide a conduit into the community where overdoses occur. Prehospital events and circumstances surrounding opioid-related overdoses provide unique opportunities to collect evidence that can contribute to prevention, harm reduction and health promotion efforts. Using data extracted from the Ottawa Paramedic Service (OPS), this proof-of-concept study demonstrated that paramedic response data were useful in providing near real-time epidemiological information (person, time and place) on the opioid epidemic and in assessing trends and opportunities to develop alert triggers. Between January and June 2017, the OPS responded to an average of four opioid-related calls each week. On average, 0.5 mg of naloxone was administered each time. For the study period, linear trends show a small but insignificant increase in calls (p = 0.18). A higher volume of calls occurred between April 16 and 29, 2017. According to local media reports, this spike in paramedic responses was due to the arrival of high-grade fentanyl in Ottawa. With further validation, paramedic data can potentially provide a novel data source to monitor opioid-related overdoses.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".