Country‐level mentoring for advanced practice nursing: A case study
Bibliographic record
Abstract
AIM: To describe the mentoring process between the ICN Advanced Practice Nurse Network practice subgroup and the University of Pécs to support the emerging advanced practice role in Hungary, and explore the creation of a mentoring algorithm for faculty and other key stakeholders worldwide who wish to develop advanced practice nursing programs. BACKGROUND: Advanced practice nurses provide comprehensive clinical care and expand access to care in more than 70 countries. In March of 2017, a representative of the Faculty of Health Sciences of the University of Pécs requested assistance in curricula development for the inaugural advanced practice nursing program in Hungary. METHODS: A mixed-methods single case study was undertaken. The sources of evidence include interviews, e-mails, review of the literature, and related documents. Qualitative data were analyzed for content, and frequencies were calculated for quantitative indicators. FINDINGS AND DISCUSSION: The findings highlight the importance of clear communication, development of shared goals, and determination to see the project through. Enriching information was provided by colleagues from diverse global settings. Credibility was gained in Hungary from the support of national and international experts. CONCLUSION: The mentoring foundation and process facilitated the role development in Hungary and contributed to an increased understanding of advanced practice nurses' scope of practice. The intentional approach and the careful ongoing reflection may lead to future successful endeavors. Multinational engagement and collaborations will promote advanced practice nursing contributions globally. IMPLICATIONS FOR NURSING POLICY: Mentoring can effectively empower nurses and advanced practice nurses to work to their full capacity. The shared experiences of international mentoring colleagues can contribute to and support the development and acceptance of national policies for the advanced practice nursing roles.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".