Psychometric evaluation of the Persian version of the Sense of Belonging in Nursing School (SBNS): a quantitative and cross-sectional design
Abstract
This study aimed to evaluate the psychometric indicators of the Persian version of the Sense of Belonging in Nursing School scale (SBNS). The study conducted in Shahroud and Semnan schools of nursing and midwifery in Iran examined nursing students using a cross-sectional approach by convenience sampling method from 3/6/2023 to 24/8/2023. To assess the SBNS scale, the forward–backward procedure was used to translate it into Persian. Face and content validity were evaluated, and exploratory and confirmatory factor analyses were conducted with sample sizes of 200 and 182, respectively. Reliability was assessed using Cronbach's alpha coefficient, MacDonald's omega, and intra-class correlation coefficient. The exploratory factor analysis resulted in the exclusion of four items, leaving a final selection of 15 items. These items were categorized into three factors: classmates, clinical staff, and inclusive educational environment, which accounted for 49.16% of the overall variance. The confirmatory factor analysis indicated that the model was a good fit for the observed data, and the subscales had high internal consistency (Cronbach's alpha coefficient was 0.752 to 0.880) and stability (intra-class correlation coefficient was 0.889 to 0.968). According to the results, it can be concluded that the Persian version of the SBNS scale demonstrates sufficient validity and reliability in assessing students' sense of belonging to the nursing school.
How strong is the evidence?
Evidence Hub reads this as a cross-sectional. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
Read the paper
Related studies
- eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses
- Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer
- OptiType: precision HLA typing from next-generation sequencing data
- Nicotinic Cholinergic System and COVID-19: In Silico Identification of an Interaction between SARS-CoV-2 and Nicotinic Receptors with Potential Therapeutic Targeting Implications
- Saccharomyces cerevisiae cells have three Omega class glutathione S-transferases acting as 1-Cys thiol transferases
- Hemolysis at low blood flow rates: in-vitro and in-silico evaluation of a centrifugal blood pump
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.