The psychometric properties of the Persian version of the innovation support inventory (ISI-12) in clinical nurses: a methodological cross-sectional study.

Hashemian Moghadam, Azam; Nemati-Vakilabad, Reza; Imashi, Reza; Yaghoobi Saghezchi, Roghayeh; Dolat Abadi, Pouya; Jamshidinia, Mobina; Mirzaei, Alireza

Background: Innovation in nursing involves applying new knowledge to create novel ideas, methods, or technologies, resulting in higher-quality care and improved patient outcomes. Adequate support for innovation is crucial for progress in nursing. This study aimed to translate the Innovation Support Inventory (ISI-12) into Persian and assess its psychometric properties specifically among clinical nurses. Methods: A methodological cross-sectional study was conducted from September 2022 to July 2023 to evaluate the face, content, and construct validity of the ISI-12. Construct validity was assessed through confirmatory factor analysis (CFA) and convergent and discriminant validity evaluation using data obtained from 321 clinical nurses. The test–retest stability and internal consistency of the ISI-12 were also evaluated to assess its reliability. Results: The Persian version of the ISI-12 validation through confirmatory factor analysis has confirmed its fit with the proposed three-factor model. The ISI-12 demonstrated high reliability, as evidenced by a Cronbach's alpha coefficient (α = 0.969), McDonald's omega coefficient (ω = 0.922), coefficient H (H = 0.979), and mean inter-item correlation (ρ = 0.418). Additionally, the stability of the ISI-12 during two weeks among 40 clinical nurses was found to be excellent, with an ICC of 0.951. Conclusion: The Persian version of the ISI-12 is a valid inventory for evaluating the innovation Support of clinical nurses.

IRAN; CROSS-sectional method; MULTITRAIT multimethod techniques; CRONBACH'S alpha; QUESTIONNAIRES; RESEARCH methodology evaluation; RESEARCH evaluation; DESCRIPTIVE statistics; NURSE practitioners; CREATIVE ability; PSYCHOMETRICS; RESEARCH; RESEARCH methodology; STATISTICAL reliability; INTRACLASS correlation; TEST validity; FACTOR analysis; DATA analysis software; DISCRIMINANT analysis

BMC Nursing, 2024, Vol 23, Issue 1, p1

1472-6955

Academic Journal

10.1186/s12912-024-02372-3