Nurse Practitioner Perspectives of Setting-Specific Opioid Prescribing Guidelines in Ontario: A Qualitative Study
Bibliographic record
Abstract
Aim: To investigate the views of Nurse Practitioners (NPs) regarding the development of setting-specific opioid prescribing guidelines. Background: There has been a rise in opioid-related deaths and adverse events in Canada in recent years. Although NPs make up a significant opioid prescriber group, no Canadian setting-specific guidelines exist to help guide their opioid prescribing. Methods: Purposive sampling was used to select NPs practicing in inpatient settings. One-on-one semi-structured interviews were conducted virtually with 14 NPs (n=14) from Ontario. Directed qualitative content analysis was used to interpret interview data. Findings: The majority of NPs interviewed (n=13, 92.8%) believe that setting-specific opioid prescribing guidelines would benefit their practice. Interview responses illustrated that NPs face several challenges associated with inpatient opioid prescribing. To help mitigate these challenges, they often consult a pharmacist and reference prescribing resources such as Lexicomp and UpToDate. Additionally, several NPs in our study formulated their own setting-specific opioid prescribing resources to help guide their prescribing. Furthermore, NPs who had transitioned to acute care from a primary health care setting experienced a lack of confidence with opioid prescribing in a specialty setting. Conclusion: The development of setting-specific NP opioid prescribing guidelines in Canada can improve the prescribing process for NPs by providing them with clear, evidence-based recommendations, thus reducing ambiguity and promoting positive patient outcomes. Future research should explore the possibility of adapting internationally published setting-specific opioid prescribing guidelines for Canadian practice and the unique opioid prescribing challenges NPs experience when transitioning from primary care to acute care settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".