The Organisation and Implementation of Nurse‐Led Clinics: Lessons Learned From the Experiences in Five Countries
Bibliographic record
Abstract
AIM: To describe the organisation of nurse-led clinics and the factors facilitating or hindering their implementation based on experiences in five countries. DESIGN: Descriptive multimethod study. METHODS: We analysed policy documents, nursing competency profiles and scientific literature and conducted 27 semi-structured interviews with stakeholders from the Netherlands, Ontario, Ireland, France and Finland between April and June 2023. We summarised relevant information on nurse-led clinic organisation in categories and mapped contextual factors following the Context and Implementation of Complex Interventions framework. RESULTS: In the Netherlands, Ontario and France, nurse-led clinics are implemented in all care settings. In all regions, clinics are led by nurses with varied educational backgrounds, but master-trained advanced practice nurses have more autonomy than bachelor-trained nurses. In France and Ireland, expanded scope of nursing practice is expected to be formally documented in a practice agreement or protocol. In all regions, nurses can prescribe medication under specific conditions. Interviewees stressed the relevance of continuous education for nurses and clear role delineation to facilitate the implementation of nurse-led clinics and collaboration with physicians. Organisational readiness, practical support and research to demonstrate quality, safety and cost-effectiveness of nurses' expanded roles were drivers of successful nurse-led clinic integration. CONCLUSION: Nurse-led clinics operate across various care settings and are staffed with nurses from diverse educational backgrounds, requiring adequate training and experience for autonomous practice. Successful implementation depends on clear role delineation, close collaboration with healthcare professionals, and supportive educational, legal and financial frameworks to ensure sustainable integration. IMPACT: Our comprehensive description of the organisation of nurse-led clinics-including legal, financial, educational and practical aspects- along with our analysis of contextual factors supporting their implementation, provides guidance to policymakers and healthcare organisations considering the successful and sustainable adoption of this model of care within their healthcare system. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".