Nurse Practitioner Opioid Prescribing and Educational Requirements in Canada and the Unites States: A Narrative Review
Bibliographic record
Abstract
Introduction/Background: Canada and the U.S had the highest level of opioid consumption per capita worldwide in 2015 (Pasricha et al., 2018). Nurse Practitioners (NPs) are authorized to prescribe opioids in both Canada and the U.S.Purpose: This narrative review aims to examine the differences in NP opioid-related educational requirements and prescribing patterns between the U.S. and Canada. Methods: A narrative review was used to synthesize findings from literature obtained through computerized databases, authoritative texts, and hand searches. Discussion: As of 2010, NPs in Canada and the U.S. must hold a master's degree. American NPs must obtain a Drug Enforcement Administration(DEA) license to prescribe opioids; the Canadian government authorized NPs to prescribe opioids in 2012, with varying provincial licensure requirements. New American national guidelines for prescribing opioids for chronic pain were released by the Centers for Disease Control (CDC) and Prevention in 2016; McMaster University in Canada followed and published 'The 2017 Canadian Guideline for Opioids for Chronic Non-Cancer Pain.'. In contrast to Canada, NP opioid prescribing in the U.S. is monitored though the DEA drug monitoring program, and NPs complete a national survey every 5 years regarding all prescribing practices. Canada lacks emergency department (ED) specific opioid prescribing guidelines whereas 24 American states have implemented them. Implications for Future Research: Canadian NP opioid prescribing is under-researched. Further research is needed to provide a more adequate comparison with American data. Additionally, research regarding ED specific guidelines could provide valuable information to guide prescribers in this rapidly changing, high-stress environment.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".