Nurse Practitioners and Controlled Substances Prescriptive Authority: Improving Access to Care
Bibliographic record
Abstract
In 2007, Health Canada proposed a new framework to regulate prescriptive authority for controlled substances, titled New Classes of Practitioners Regulations (NCPR). The new regulatory framework was passed in November 2012; it gives nurse practitioners (NPs), midwives and podiatrists the authority to prescribe controlled medications under the Controlled Drugs and Substances Act. It is expected that authorizing NPs to write prescriptions for certain controlled substances commonly used in primary care will enhance flexibility and timeliness in primary care service delivery. Studies from the United States have shown positive outcomes in primary care access, decreased healthcare costs and the evolution and advancement of the NP role when prescriptive authority was expanded to include controlled substances. The purpose of this paper is to examine how NPs' prescriptive authority for controlled substances affects access to primary care and NP role development. Three key issues identified from the experience of one group of NPs in the United States (access to care, professional autonomy and prescriber knowledge) offer insight into the practice changes that may be anticipated for NPs in Canada now that they have acquired prescriptive authority for controlled substances. Recommendations are offered to assist nurse leaders and educators to best support NPs as they take on this new and important role responsibility.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".