An international e-Delphi study to identify core competencies for Italian cardiac nurses
Bibliographic record
Abstract
AIMS: The management of cardiovascular patients requires increasingly competent nursing professionals. In Italy, there are no specific postgraduate courses focused on specialist cardiac skills development for nurses. To develop such courses, content incorporating appropriate competencies is required and this study was designed to meet this. To delineate a set of core competencies to develop national educational interventions to ensure cardiac nurses in Italy achieve international standards. METHODS AND RESULTS: A three-round e-Delphi study including a panel of 32 expert cardiac nurses from the UK, Canada, Australia, New Zealand, and Italy was conducted; 26 respondents completed all three rounds. The first round sought a list of five competencies from each participant which they were asked to prioritize in Round 2. In Round 3, they were asked to prioritize again with the knowledge of the priorities identified in Round 2. The final list of competencies was those achieving 70% agreement among participants. We identified 14 core competencies spanning a range of areas of competence including technical, interpersonal, health promotion, use of evidence, and management. Only minor differences were evident between the Italian and the international panel regarding the priority given to some core competences, such a leadership and taking patient history. CONCLUSION: This is the first study in Italy to delineate cardiac nurses' core competencies. As such, it provides a foundation for the development of postgraduate educational programmes for cardiac nurses including competencies that are congruent with international standards.
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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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".