Competency Development to Support Safe Nurse Practitioner Prescribing of Controlled Drugs and Substances in British Columbia
Bibliographic record
Abstract
In 2012, Canada passed legislation giving nurse practitioners (NPs) authority to prescribe controlled drugs and substances. Steps toward safe implementation by the nursing regulatory body in British Columbia included development of controlled drugs and substances prescribing competencies for use in educating and authorizing NPs for this new scope. In this article, we discuss the development and refinement of the competencies, specifically their application to nursing regulation in British Columbia. Methods include incorporation of the Competency Outcome Performance Assessment Model as a guiding theoretical framework. Over two meetings in 2014, a small representative panel of health professionals completed face and content validation of 17 initial competencies using a visual Likert-type scale ranking process (1-5, unnecessary to essential) with Google Docs for real-time comparative refinement. The resulting 10 competency statements provide the foundation for outcome indicator development which will be used in NP education and the regulatory body's regulation and remediation processes. Finally, we describe the policy process applied to implement competencies for NP controlled drugs and substances prescribing and the subsequent challenges of implementation of controlled drugs and substances authority in British Columbia. The article concludes with an overview of lessons learned that may be beneficial to health professions regulatory bodies introducing or expanding prescribing scope for NPs.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".