BMC Nursing (May 2024)

A scale for measuring nursing digital application skills: a development and psychometric testing study

  • Shijia Qin,
  • Jianzhong Zhang,
  • Xiaomin Sun,
  • Ge Meng,
  • Xinqi Zhuang,
  • Yitong Jia,
  • Wen-Xin Shi,
  • Yin-Ping Zhang

DOI
https://doi.org/10.1186/s12912-024-02030-8
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 10

Abstract

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Abstract Background The adoption of digitization has emerged as a new trend in the advancement of healthcare systems. To ensure high-quality care, nurses should possess sufficient skills to assist in the digital transformation of healthcare practices. Suitable tools have seldom been developed to assess nurses’ skills in digital applications. This study aimed to develop the Nursing Digital Application Skill Scale (NDASS) and test its psychometric properties. Methods The Nursing Digital Application Skill Scale was developed in three phases. In Phase 1, an item pool was developed based on previous literature and the actual situation of nursing work. Phase 2 included 14 experts’ assessment of content validity and a focus group interview with 30 nurses to pretest the scale. In phase 3, 429 registered nurses were selected from March to June 2023, and item analysis, exploratory factor analysis, and confirmatory factor analysis were used to refine the number of items and explore the factor structure of the scale. Additionally, reliability was determined by internal consistency and test-retest reliability. Results The final version of the NDASS consisted of 12 items. The content validity index of NDASS reached 0.975 at an acceptable level. The convergent validity test showed that the average variance extracted value was 0.694 (> 0.5) and the composite reliability value was 0.964 (> 0.7), both of which met the requirements. The principal component analysis resulted in a single-factor structure explaining 74.794% of the total variance. All the fitting indices satisfied the standard based upon confirmatory factor analyses, indicating that the single-factor structure contributed to an ideal model fit. The internal consistency appeared high for the NDASS, reaching a Cronbach’s alpha value of 0.968. The test-retest reliability was 0.740, and the split-half coefficient was 0.935. Conclusion The final version of the NDASS, which possesses adequate psychometric properties, is a reliable and effective instrument for nurses to self-assess digital skills in nursing work and for nursing managers in designing nursing digital skill training.

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