A Hybrid MADM Model for Newly Graduated Nurse's Competence Evaluation and Improvement

Area of Science:

• Nursing Education
• Healthcare Management
• Decision Science

Background:

• Hospitals need to evaluate the practical competency of newly graduated nurses.
• Current competency assessments rely on qualitative attributes and expert knowledge.
• Systematic evaluation frameworks are crucial for professional development.

Purpose of the Study:

• To propose a hybrid multiple attribute decision-making (MADM) model for evaluating newly graduated nurses' practical competency.
• To establish a framework for identifying and reducing performance gaps.
• To provide a systematic approach for nursing departments to improve nurse competencies.

Main Methods:

• Utilized a hybrid MADM model combining DEMATEL-based ANP (DANP) and MOORA-AS.
• Employed a causal influence-network diagram (CIND) with expert-derived weights.
• Applied the model to empirical data from Chinese hospitals.

Main Results:

• The proposed hybrid MADM model demonstrated reliable results in evaluating nurse competency.
• The CIND facilitated systematic identification of performance ratio-gaps.
• The model enables nursing managers to systematically improve competencies.

Conclusions:

• The hybrid MADM model offers an effective evaluation framework for newly graduated nurses' practical competency.
• This approach supports nursing departments in systematically enhancing the skills of new nurses.
• The study provides a valuable tool for healthcare management and nursing education.