Nurse Practitioner Opioid Prescribing and Safety Measure Utilization Patterns in Ontario
Bibliographic record
Abstract
Background: In 2012, the Canadian federal government amended the Controlled Drugs and Substances Act authorizing nurse practitioners (NPs) to prescribe controlled substances. Ontario was the last province to adopt this legislation in 2017. Despite this change, a significant gap exists in research seeing as there are currently no published studies regarding NP opioid prescribing patterns (Pittman et al., 2020). Purpose: The qualitative aspect of this mixed-methods study aims to further explain the quantitative findings. Qualitative interview data provided insight into the impact of NP practice setting and patient population on survey responses. Content analysis will illustrate the key factors influencing NP opioid prescribing and safety measure utilization patterns. Methods: 262 participants completed the survey and 153 agreed to qualitative follow-up. NPs across Ontario (n=14) selected from various geographical locations agreed to be interviewed for the qualitative component. Results: Areas explored included prescribing preference for specific opioids, the impact of practice setting on utilization of safety measures, and the perceived effectiveness of suggested safety measures. Three key findings emerged: (a) hydromorphone is the preferred opioid due to a better reported side effects profile when used in small dosages, especially in the elderly (b) NPs practicing in hospital settings use safety measures less often than community counterparts as a result of organizational structures (c) limiting supply is considered the most effective safety measure. Implications for Future Research: A follow-up study focusing on a cohort of NPs practicing in emergency departments could illustrate how this unique practice setting impacts NP opioid prescribing.\nReferences\nPittman, G., Morrell, S., & Ralph, J. (2020). Opioid prescribing safety measures utilized by primary healthcare providers in Canada: A scoping review. Journal of Nursing Regulation, 10(4), 13-21. https://doi.org/10.1016/S2155-8256(20)30009-0
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".