ABSTRACT
BACKGROUND: Difficulties in adapting to the workplace can affect newly graduated nurses' transition. Such nurses must adapt quickly, as it can affect their future career prospects. Therefore, this review aimed to identify the success factors that promote newly graduated nurses' effective transition and adaptation. METHODS: The Joanna Briggs Institute scoping reviews methodology was used. Data were extracted from MEDLINE, Scopus, EBSCOhost, and Web of Science publications published between 2011 and 2020. A total of 23 articles were included in this review, which comprised qualitative, quantitative, and mixed methods primary research studies focusing on the contributing factors that aided newly graduated nurses' adaptation to the work environment during their transition period. Key emerging themes were identified with thematic analysis. RESULTS: Three main themes were identified: (1) organisational contribution (social development, organisational culture, work characteristics, work readiness, work commitment, professional role), (2) personality traits (self-embodiment, personality masking, being proactive and confident), and (3) academic institutions (pre-entry knowledge and role of nursing faculty). Newly graduated nurses' adaptation should begin during nursing education, be supported by the workplace organisation, and driven by the nurse's personality. We determined that that the role of nursing education in aiding the provision of the required knowledge and actual clinical experiences to students profoundly affected developing nurses' self-confidence levels in delivering nursing care effectively. Additionally, a warm environment supported nurses emotionally and physically. CONCLUSIONS: While organisations and educational institutions have undertaken numerous efforts to ensure that newly graduated nurses are adequately supported, the nurse's personality and values are also equally important to ease adaptation during the transition process. Academic and workplace programs designed for newly graduated nurses should apply and emphasise this knowledge to develop and strengthen their personalities and values, especially to increase confidence and promote proactive values that facilitate newly graduated nurses' rapid and effective adaptation to their new employment.
ABSTRACT
A long-established approach, Confirmatory Factor Analysis (CFA) is used to validate measurement models of latent constructs. Employing CFA can be useful for assessing the validity and reliability of such models. The study adapted previous instruments and modified them to suit the current setting. The new measurement model is termed NENA-q. Exploratory factor analysis (EFA) revealed the instruments of the NENA-q model formed a construct of the second order with four dimensions, namely organizational contribution (OC), academic institution contribution (AIC), personality traits (PT), and newly employed nurses' adaptation (NENA). Researchers administered the questionnaires to a sample of 496 newly employed nurses working in hospitals under the Ministry of Health (MOH) for the confirmation of the extracted dimensions. The study performed a two-step CFA procedure to validate NENA-q since the model involves higher-order constructs. The first step was individual CFA, while the second step was pooled CFA. The validation procedure through confirmatory factor analysis (CFA) found the model achieved the threshold of construct validity through fitness index assessment. The model also achieved convergent validity when all average variance extracted (AVE) exceeded the threshold value of greater than 0.5. The assessment of the composite reliability (CR) value indicates all CR values exceeded the threshold value of 0.6, which indicates the construct achieved composite reliability. Overall, the NENA-q model consisting of the OC construct, AIC construct, PT construct, and NENA construct for CFA has met the fitness indexes and passed the measurements of the AVE, CR, and normality test. Once the measurement models have been validated through CFA procedure, the researcher can assemble these constructs into structural model and estimate the required parameter through structural equation modelling (SEM) procedure.
