Registered Nurse Prescribing in Ontario: Learners’ Perspective
Bibliographic record
Abstract
Background: In response to evolving health care demands and the increasing need for accessible patient care, eight Canadian provinces have expanded the scope of practice for registered nurses (RNs) to prescribe medications. Ontario is the latest province to approve RN prescribing. RNs with prescribing authority in Ontario can prescribe specific medications approved by the Ministry of Health and Long-Term Care and authorized by the College of Nurses of Ontario (CNO). RNs must complete one of the four available CNO Council–approved RN prescribing education programs to become an authorized prescriber in Ontario. As RN prescribing is new to Ontario, it is essential to assess the effectiveness of the educational programs preparing nurses for this new role. Purpose: A quality improvement project (QIP) was conducted to gain learners’ perspectives on one of Ontario’s CNO-approved RN Prescribing programs. Methods: This QIP focused on learners enrolled through the University Consortium RN Prescribing Program. Pre- and post-course surveys were administered via Qualtrics XM. Data collected included demographic information, motivation for enrolling in the program, employment plans related to the expanded scope of practice, familiarity with the new CNO Practice Standards, and confidence in prescribing safely. Pre- and post-course survey composite scores were computed to reflect participants’ overall familiarity with the RN prescribers’ scope of practice and their confidence in their ability to prescribe safely within the scope of practice. We conducted Mann-Whitney U non-parametric tests were conducted to determine any statistical difference between the median of the pre- and post-course composite scores (familiarity scores and confidence level). Results: A total of 194 pre-course and 136 post-course anonymous survey questionnaires were completed through Qualtrics XM. The baseline demographic characteristics were similar in pre- and post-course groups. Top motivators for taking the course were career advancement and personal goals. The median familiarity score for pre-course (2.50) and post-course (3.0) surveys was statistically significant (U = 4141.5, z = –10.93, p < 0.001). The median confidence score for pre-course (80.5) and post-course (85.0) surveys was not statistically significant (U = 11629, z = –1.843, p = –0.065). Conclusion: Upon completion of the course, learners in the University Consortium’s inaugural RN prescribing program reported increased familiarity with the scope of practice and confidence in their ability to prescribe safely. Introducing safe registered RN prescribing in Ontario can significantly advance health care by optimizing resource use, improving access to care, and enhancing patient-centred care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".