Prescribing Opioids in Primary Care Settings: Experiences of Nurse Practitioners
Bibliographic record
Abstract
Abstract Many Canadians access health care for the management of acute or chronic pain. Therapeutic pain management approaches provided by nurse practitioners (NPs) may involve non-pharmacological options and the prescription of medications. When opioids are prescribed by NPs, there is a need for awareness of the concerns for opioid abuse and development of dependence, misuse related to a lack of medication education, diversion of the medication for potential financial gain, and obtaining opioids illegally when prescriptions are tapered or discontinued: which all have been implicated for concerns regarding opioid prescribing and the present opioid crisis (Canadian Centre on Substance Use and Addiction, 2020). The purpose of this study was to explore the experiences of the NPs who prescribe opioids in primary care settings within the province of Saskatchewan. The following question guided this study: What are the experiences of NPs who prescribe opioids in primary care settings? Interpretive Description was chosen as the guiding method for this inquiry: as interpretive description is most often used to explore practice based clinical questions. Through the use of the scaffolding approach in Interpretive Description (Thorne, 2016), a scoping review of the literature was performed that identified themes across a small number of peer reviewed articles from international studies, and a gap in the Canadian literature on the study phenomena. The qualitative inductive approach of Interpretive Description was chosen to develop nursing knowledge about opioid prescribing by NPs using interview data collection and analysis. Information about the study and a link to a recruitment survey was distributed to NPs in collaboration with a provincial regulatory body. The recruitment survey asked respondents to complete demographic and practice questions about their practice of opioid prescribing and indicate their interest in participating in an interview on the topic. This purposive sampling method used a modified Dillman approach, recruited 21 volunteers to conduct semi - structured interviews (Dillman et al., 2014). Constant comparative analysis of the interview data resulted in two focus areas of thematic development: the practice concerns involved in prescribing opioids and the decision-making process employed by NPs in addressing pain management. Findings from the thematic analysis of practice experiences when prescribing opioids identified three primary themes: learning to prescribe, gaining competence and confidence, and experiencing concerns for personal safety. A second descriptive thematic analysis explored the participants’ decision-making when prescribing opioids in primary care clinical settings. This analysis identified three themes that influence participant decision-making in practice: negotiating practice autonomy boundaries, applying clinical practice guidelines, and retribution from authorities. Findings from the analyses suggests that participants with more years of experience felt their educational preparation was appropriate; however, participants with fewer years of experience felt hesitant and underprepared for the associated level of accountability and responsibility. The outcomes identified a need for increased knowledge and support for NPs when prescribing opioids and a need for policy change within electronic health records to identify clients with opioid contracts to mitigate the potential of opioid prescription abuse, misuse, and diversion.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".