Bibliographic record
Abstract

 Overdose deaths have been occurring at high rates in many parts of Canada. From January 2016 (when national surveillance began) to March 2019, an estimated 12,800 Canadians died of an opioid overdose.1 In addition to opioid-related harms, stimulants such as methamphetamine have re-emerged in some regions and are also contributing to the current rise in overdose deaths.
 COVID-19 has resulted in a more compromised illicit drug supply, and those who use drugs have had limited access to formal and informal supports because of public health measures regarding physical distancing. As a result, overdose deaths have increased during the pandemic.
 Harm reduction approaches provide a mechanism to prevent overdose deaths and have additional health and public safety benefits. The current crisis has been exacerbated by COVID-19; therefore, it is an appropriate time to consider the entire continuum of harm reduction approaches available to reduce preventable overdose deaths.
 People with lived experience of drug use should be meaningfully included in policy discussions about harm reduction and overdose prevention interventions. This would enhance the person-centredness of programs and ensure they are reflective of the lived realities of those who use drugs.
 Although societal attitudes about drug use are changing, harm reduction interventions remain politically contentious. Countering stigma, being prepared to engage with community concerns, and clearly articulating that harm reduction services are intended to complement and not replace drug treatment are all important in enhancing public understanding of harm reduction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".