<i>Facilitators</i> and Barriers to Nurse Practitioners Prescribing Methadone for Opioid Use Disorder in Nova Scotia: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Opioid use has escalated dramatically resulting in an increase in deaths. Access to treatment for opioid use disorder (OUD) is poor. The addition of nurse practitioners (NPs) as prescribers of methadone for OUD offers potential for improving access. Little is known about what support NPs will require as they prescribe methadone. PURPOSE: This paper identifies facilitators and barriers to NPs prescribing methadone. METHODS: In this qualitative study, in-person and phone semi-structured interviews were conducted with 18 participants. Participants included NPs (n=5), physicians (n=5), and stakeholders including members of professional regulatory bodies and government, academics and other clinicians (n=8). Interviews were recorded, transcribed, and analyzed using thematic analysis and software (NVivo 12.4.0) for data management. RESULTS: Four themes emerged: 1) Pervasive Barrier of Stigma; 2) Perceived Complexity of Patients Living with OUD; 3) NP Education and Practice Supports and; 4) Health Care Context and NP Role Implementation. CONCLUSIONS: Barriers and facilitators to NP prescribing are similar to those encountered by physicians. Factors unique to NPs include the identification of role clarity as a facilitator and navigation of physician networks as a barrier. Research conducted with current NP methadone prescribers is required to evaluate implementation of this service.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".