Bibliographic record
Abstract
The extent and impact of the opioid crisis in Canada The opioid crisis in Canada has been an issue for nearly a quarter of a century. Here, Norm Buckley and Jason Busse from the Michael G DeGroote Institute for Pain Research and Care, and the National Pain Centre discuss the issue, its effects both general and on specific communities, and what can be done about it. Over the past twenty-five or so years, there has been a remarkable arc in the medical use of opioids in Canada, from prescribing for only very specific instances (e.g., end-of-life care) to much more widespread use (e.g., chronic noncancer pain), and now there is greater reluctance to provide opioid analgesia. This coincides with growing awareness of the unmet need for chronic pain care and promotion of the use of opioids by commercial interests. Production of sustained-release forms of opioids containing large doses, along with widespread use, was associated with the diversion of prescription opioids into illicit or recreational markets, and opioid-related deaths began to be more frequently associated with these drugs rather than historic illicit drugs such as heroin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".