LAUSR

AIM To develop and psychometrically test shortened 37-item Chinese Work Readiness Scale for Graduate Nurse without compromising its reliability and validity. DESIGN Two cross-sectional studies. METHODS Data were obtained from two cross-sectional online surveys on the same target group of newly graduated nurses in China. The two periods of data collection were from June to August 2018 and from June and July 2021, with 1348 respondents completing the 37-item Chinese Work Readiness Scale for Graduate Nurse. The optimal test assembly procedure based on a generalized partial credit model was adopted to shorten the scale. RESULTS The mean age of the 1348 nurses was 22.3 years (standard deviation = 1.5), with 1257 (93.2%) being female. The category characteristic curve showed that a 5-point Likert scale was adequate in lieu of the original 10-point Likert scale. The optimal test assembly procedure identified a 17-item version that maintained at least 0.9 correlation with the full scale, a Cronbach's alpha of at least 0.9, and at least 70% of the test information retained. Its discrimination parameters ranged from 1.68 to 2.84, whereas the difficulty parameters were monotonic and ranged from -4.50 to 1.56. When retaining only 50% of test information, a 9-item version was obtained. Its discrimination parameters ranged from 1.84 to 4.55, and difficulty parameters ranged from -3.37 to 1.15. The Cronbach's alphas for the 17-item and 9-item versions were 0.97 and 0.95 respectively. CONCLUSIONS Both the 17-item and 9-item versions are appropriate for assessing registered nurses' work readiness, with the 9-item version demanding more subjects to compensate for the reduced information. Optimal test assembly approach is appropriate in shortening self-reported scales in the nursing field, while retaining satisfactory levels of reliability, validity, test information and conceptual adequacy. PATIENT OR PUBLIC CONTRIBUTION This study involved graduate nurses who provided responses to the test scale and experts who contributed to the content validation.