Addressing the Opioid Crisis—The Need for a Pain Management Intervention in Community Pharmacies in Canada: A Narrative Review
Bibliographic record
Abstract
Background: The opioid crisis is a public health concern in Canada with a continued rise in deaths and presents a significant economic impact on the healthcare system. There is a need to develop and implement strategies for decreasing the risk of opioid overdoses and other opioid-related harms resulting from the use of prescription opioids. Pharmacists, as medication experts and educators, and as one of the most accessible frontline healthcare providers, are well positioned to provide effective opioid stewardship through a pain management program focused on improving pain management for patients, supporting appropriate prescribing and dispensing of opioids, and supporting safe and appropriate use of opioids to minimize potential opioid misuse, abuse, and harm. Methods: A literature search was conducted in PubMed, Embase and grey literature to determine the characteristics of an effective community pharmacy-based pain management program, including the facilitators and barriers to be considered. Discussion: An effective pain management program should be multicomponent, address other co-morbid conditions in addition to pain, and contain a continuing education component for pharmacists. Solutions to implementation barriers, including pharmacy workflow; addressing attitudes beliefs, and stigma; and pharmacy remuneration, as well as leveraging the expansion of scope from the Controlled Drugs and Substances Act exemption to facilitate implementation, should be considered. Conclusions: Future work should include the development, implementation, and evaluation of a multicomponent, evidence-based intervention strategy in Canadian community pharmacies to demonstrate the impact pharmacists can have on the management of chronic pain and as one potential solution to helping curb the opioid crisis. Future studies should measure associated costs for such a program and any resulting cost-savings to the healthcare system.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".