Lessons learned from the opioid crisis across the pillars of the Canadian drugs and substances strategy
Bibliographic record
Abstract
BACKGROUND: Canada is facing an urgent challenge to reduce the harms associated with opioids: from January 2016 to December of 2018, more than 11,500 individuals lost their lives due to opioid related harms. This review examines responses to the opioid crisis thus far, the lessons learned from these initiatives and the knowledge gaps that still need to be addressed across the four pillar model adopted by the CDSS. METHODS: A search of peer-reviewed literature was conducted in PubMed and PsycNet, and grey literature was retrieved from reputable substance use and health organizations to determine responses to the opioid crisis and related outcomes between 2013 and 2019. Findings related to actions, outcomes and unintended consequences across the categories of prevention, treatment, harm reduction, enforcement and the evidence base were included and synthesized into a narrative review on lessons learned. RESULTS: The opioid crisis is a result of multiple, complex interrelated factors. Many physicians may not feel competent to appropriately treat pain and/or addiction. Pushes for opioid deprescribing have resulted in some individuals using illicit opioids as treatment. A range of effective and accessible pharmacological and psychological treatments are still required. When regulations are barriers, unsanctioned actions, such as overdose prevention sites, may be enacted by individuals to respond to urgent public health needs. A nimble response with evolving enforcement perspectives can aid individuals experiencing harms from opioid use. CONCLUSIONS: There is no one size fits all response to this crisis, and consideration should be given to the unique needs of different communities and populations, as well as the broader impact of harms on families, communities, and society. A situation so multifaceted requires both immediate and long-term strategies implemented concurrently in order to address the differing and on-going needs of Canadians experiencing opioid harms. The expertise of individuals and families affected by the opioid crisis must be included in consultations and decisions related to different strategies, to ensure responses are not stigmatizing, that they will be effective and acceptable and that unintended consequences are quickly recognized and mitigated.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| 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".