A survey of nurse practitioner controlled drugs and substances prescribing in three Canadian provinces
Bibliographic record
Abstract
AIMS AND OBJECTIVES: In Canada, nurse practitioners (NP) were legally authorised to prescribe controlled drugs and substances (CDS) in 2012. The objective of this study was to understand current NP-CDS prescribing in Alberta, Manitoba and Saskatchewan, Canada. This study is a component of a larger three-phase survey of NP practice patterns in these same provinces. BACKGROUND: Nurse practitioners are nurses with a graduate degree who have the legal authority to perform expanded functions in health systems, including prescribing CDS. Given the novelty of CDS prescribing for NPs in Canada, little is known about this component of their role. DESIGN: A secondary analysis of survey data collected between March 2016 and May 2017 was used to examine NP-CDS-prescribing patterns and identify potential associated factors. METHODS: Nurse practitioners in Alberta, Manitoba and Saskatchewan were invited to complete a professional practice pattern survey. The survey was administered through a secure electronic data collection software application (redcap). In the practice pattern survey, 42 variables from 15 distinct conceptual questions were analysed in this study as potential predictors of NP-CDS prescribing within a purposeful selection ordinal logistic regression model. This scientific submission has been assessed for accuracy and completeness using the Equator STROBE guideline criteria (see Appendix S1). RESULTS/FINDINGS: Five variables were found to be associated with an increased odds of more frequent NP-CDS prescribing in addition to three confounders/clinically relevant variables. Factors commonly associated with an increased frequency of NP-CDS prescribing relate to location of practice, area of practice, previous nursing experience, team environments and common diagnoses. CONCLUSION: Little is known about NP-CDS prescribing. Understanding this important component of the NPs emerging legal scope of professional practice can contribute to the continued refinement of this role as well as support ongoing enquiry into the causes of, and potential interventions to prevent, the present opioid overdose deaths occurring while under an active prescription. RELEVANCE TO CLINICAL PRACTICE: Understanding factors that influence NP-CDS prescribing has relevance to the current drug-related prescription fatalities crisis in all countries.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".