RN and RPN Perceptions and Experiences of Prescribing Opioid Agonist Therapy to People with an Opioid Use Disorder in British Columbia
Bibliographic record
Abstract
Study backgroundBritish Columbia (B.C.) has suffered a significant loss of life every day due to the unregulated drug poisoning crisis that has affected this province since 2016 (B.C. Coroners Service, 2023). In September 2020 the B.C. Provincial Medical Health Officer, issued a provincial health order to allow registered nurses (RNs) and registered psychiatric nurses (RPNs) to diagnose and prescribe pharmacological treatment for opioid use disorder (OUD) (Ministry of Health, 2020).PurposeTo understand how RNs and RPNs in B.C. experience their expanded role as nurse prescribers of opioid agonist therapy (OAT).MethodsUtilizing Sally Thorne's (2016) Interpretive Description method, a purposeful sample of RNs and RPNs across the province who actively prescribe OAT to people with an OUD were interviewed about their experience and perceptions.ResultsKey findings of this study include insights into the positive and challenging experiences of prescribing OAT in B.C.; operational implementation considerations for RNs and RPNs prescribing OAT; and the strengths and flexibility that RNs and RPNs can bring to OAT care.ConclusionsFindings within this research are relevant to other Canadian provinces considering implementing RN/RPN OAT prescribing as a strategy to increase access to pharmacological treatment for people with OUD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".