Sinicization and reliability and validity test of the nursing assessment for infertility women
Objective To translate the Nursing Assessment For Infertility Women(NAFIW)into Chinese and to evaluate its reliability and validity.To assess the level of nursing needs of infertility women.Methods The NAFIW was translated into Chinese using the steps of translation,back-translation and proofreading.Experts were invited to conduct cultural adjustments on the scale,and a questionnaire survey was conducted on 162 infertile patients.Data processing methods such as item analysis,exploratory factor analysis,reliability analysis,descriptive statistical analysis,t-test,or one-way square analysis were used.Results The Chinese version of NAFIW had good reliability and validity.Factor analysis identified three common factors,including physiological and psychological dimensions,information needs,and support needs.There were a total of 15 items,with a cumulative variance contribution rate of 69.884％.The Cronbach's α coefficient was 0.905～0.913 in each dimension,and the Cronbach's α coefficient in the total scale was 0.915.The index of content validity was 0.898,the KMO test value of structural validity was 0.889,and the probability value of Bartlett spherical test P＜0.01.The overall level of nursing needs for infertile patients was relatively high,with the highest demand for information,followed by physiological and psychological needs,and the lowest demand for support.There was a significant correlation between factors such as infertility,previous pregnancy experience,and ART treatment cycle and the level of nursing needs.Conclusion The Chinese version of NAFIW can serve as an effective tool for evaluating the nursing needs of infertility patients in China.Infertility patients of different types and stages have varying degrees and aspects of nursing needs,requiring personalized evaluation and intervention by medical staff based on the specific situation of the patients.
infertilityassisted reproductive technologyassessment of nursing needsreliabilityvalidityscale
万方数据
维普
