Examining the Clinical Role and Educational Preparation of Heart Failure Nurses Across Europe. A Survey of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP) of the ESC
Bibliographic record
Abstract
AIMS: To describe the clinical practice and educational preparation of heart failure (HF) nurses across Europe and determine the key differences between countries. METHODS AND RESULTS: A survey tool was developed, in English, by the Heart Failure Association Patient Care committee of the European Society of Cardiology (ESC). It was translated into eight languages, before electronically disseminated by nurse ambassadors, presidents of HF national societies and through social media. A total of 837 nurses involved in the daily care of patients with HF from 15 countries completed the survey. Most nurses, 78% (n = 395) worked within a hospital outpatient setting, and 51% (n = 431) had access to a specialized HF multidisciplinary team. Nurses performed a range of activities including patient education to promote self-care, virtual and in-person symptom monitoring. A third had more than 5-year experience in cardiac care and 22% (n = 182) prescribed HF medications. There was a significant correlation between HF nurses that prescribed HF medications and access to a specialist multidisciplinary team (p = 0.04). A small number of nurses, mainly from Belgium, supported invasive monitoring (n = 68, 8%) with 14% (n = 120) of mostly Danish nurses supporting exercise programmes. The majority of nurses surveyed were committed to further academic professional development, with 41% (n = 343) having completed a HF course. CONCLUSION: The role of the HF nurse varies across Europe, however involvement in patient education, symptom monitoring and follow-up remain core to their practice. In specific activities including the prescribing of HF medications and involvement in invasive monitoring, practice has advanced with collaboration in the multidisciplinary team. Consequently, harmonization of education, training and career pathways are required to standardize HF care aligned with expert guidelines across Europe.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".