Staff Experiences with the Implementation of Nurse Practitioner (NP)-led Clinics in New Brunswick, Canada
Bibliographic record
Abstract
BackgroundAccess to primary care is a challenge across Canada. In the province of New Brunswick (NB), approximately 15% of citizens do not have a primary care provider (PCP). The Government of NB recently implemented clinics staffed by nurse practitioners (NPs) in various regions in the province to reduce the provincial waitlist for a PCP.PurposeThis study aimed to identify facilitators and barriers to NP-led clinic implementation, as perceived by clinic staff.MethodsUsing a cross-sectional qualitative descriptive design, data was collected using semi-structured interviews and analysed using qualitative content analysis.ResultsStudy participants included 16 employes of two NP-led clinics in NB (NPs, registered nurses (RN), licensed practical nurses (LPN), administrative staff, and managerial staff). Facilitators include having experienced mentors, collaborative practices, and well-equipped clinics. Barriers include rushed timelines, complex decision-making processes, large and complex caseloads, inadequate clinic space, and difficulty in staff recruitment and retention. Participants discussed the positive impact of NP-led clinics through improved access to primary care, resulting in reduced burdens on emergency departments and walk-in clinics. Participants recommend adding additional NP-led clinics and integrating multidisciplinary allied health professional teams to enhance care integration.ConclusionNP-led clinics are increasingly being implemented across Canada to improve primary care access, particularly in areas where there are shortages of PCPs. Findings from this study will help inform the development and implementation of other NP-led clinics across NB and Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".