The opioid crisis: past, present and future policy climate in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Addressing opioid use disorder has become a priority in Ontario, Canada, because of its high economic, social and health burden. There continues to be stigma and criticism relating to opioid use disorder and treatment options. The result has been unsystematic, partial, reactive policies and programs developed based on divergent points of view. The aim of this manuscript is to describe how past and present understandings, narratives, ideologies and discourse of opioid use, have impacted policies over the course of the growing opioid crisis. COMMENTARY: Assessing the impact of policy is complex. It involves consideration of conceptual issues of what impacts policy change. In this manuscript we argue that the development of polices and initiatives regarding opioids, opioid use disorder and opioid agonist treatment in the last decade, have been more strongly associated with the evolution of ideas, narratives and discourses rather than research relating to opioids. We formulate our argument using a framework by Sumner, Crichton, Theobald, Zulu, and Parkhurs. We use examples from the Canadian context to outline our argument such as: the anti- drug legislation from the Canadian Federal Conservative government in 2007; the removal of OxyContin™ from the drug formulary in 2012; the rapid expansion of opioid agonist treatment beginning in the early 2000s, the unilateral decision made regarding fee cuts for physicians providing opioid agonist treatment in 2015; and the most recent implementation of a narcotics monitoring system, which are all closely linked with the shifts in public opinion and discourse at the time of which these policies and programs are implemented. CONCLUSION: We conclude with recommendations to consider a multifactorial response using evidence and stakeholder engagement to address the opioid crisis, rather than a reactive policy approach. We suggest that researchers have an important role in shaping future policy by reframing ideas through knowledge translation, formation of values, creation of new knowledge and adding to the quality of public discourse and debate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".