Moving Away From a “One Size Fits All” Model: Ensuring Opioid Stewardship Includes People Who Use Drugs
Bibliographic record
Abstract
The opioid-driven overdose crisis has had devastating effects across North America, resulting from a complex interplay between individual, social-structural, and environmental factors. Changing approaches to pain management, increased heroin use, and potent synthetic opioids infiltrating the drug supply are compounded by both lack of access to opioid use disorder treatment and surrounding stigma. Inappropriate opioid prescribing practices in healthcare settings have played a central role, and in recent years, there has been increasing interest in implementing hospital-based opioid stewardship programs aimed at improving safety and monitoring opioid prescribing. There is a range of approaches taken by these programs, ranging from audit and feedback to consult services; however, a significant focus of many of these programs is on medication restriction. Such measures stand to negatively impact the care of people with complex healthcare needs, including those currently on long-term opioid therapy, and those with increased opioid tolerance. In this commentary, we emphasize the importance of creating opioid stewardship programs focused on appropriate pain treatment rather than solely on medication restriction to both appropriately prescribe to and manage pain in people who use illicit drugs. This population faces many barriers to care, such as unique dose requirements and high interpatient variability that "one size fits all" stewardship cannot appropriately address. Additionally, opioid stewardship programs that use patient-centered strategies such as multi-disciplinary consult services have been shown to lead to positive health outcomes and have significant potential to address the current shortcomings in pain management for people who use illicit drugs.
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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 | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.028 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".