Exploring Requirements of the ‘Would Be’ Expert Cardiac Care Nurse
Bibliographic record
Abstract
BACKGROUND: Cardiovascular interventions have experienced extraordinary progress in the past decades. But it seems that preparation of cardiovascular nurses has not been in pace with the changes. This is while these nurses have a prominent role in the care and management of life-threatening diseases in these wards which can lead to reduced mortality. The present study aimed to explore the effective factors on training a cardiac care nurse. METHODS: This research is qualitative and applies inductive content analysis. Participants included 7 matrons and 50 nurses selected through purposive sampling method. Data was collected using semi-structured interviews and open questionnaires. Also, conventional approach to content analysis was used to analyze data. RESULTS: To have a qualified CCU nurse, the findings cover four main themes including specialist nurses (having appropriate personal and professional characteristics), acquiring comprehensive educational content (acquiring specialized cardiac, basic nursing, and general educational content), integrated educational approach (group education, individual education and special education methods), and administrative and organizational requirements (the necessity of recognizing the position of CCU nurses, allocation of material and intellectual benefits, educational and managerial monitoring of supervisors). CONCLUSION: Since cardiac care nurses play an important role in ensuring nursing care quality in cardiovascular wards and can improve the care delivered to the patients, it is necessary for nurses to have appropriate professional and personal qualities. Therefore, nurses should have training on general, basic nursing, and specialist cardiac content through integrated educational approach. At the same time, managerial and organizational requirements should be established to maintain and improve their competencies and capabilities.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".